v1.8.0

feat(paddleocr-vl): add structured HTML output and table parsing for PaddleOCR-VL, update API, tests, and README
v1.7.1
2026-01-18 00:11:17 +00:00 · 2026-01-18 00:11:17 +00:00 · 2026-01-17 23:13:47 +00:00 · 2026-01-17 23:13:47 +00:00 · 2026-01-17 21:50:09 +00:00 · 2026-01-17 21:50:09 +00:00
23 changed files with 3021 additions and 214 deletions
--- a/2
+++ b/2
@@ -14,7 +14,7 @@ ENV OLLAMA_ORIGINS="*"
 ENV CUDA_VISIBLE_DEVICES=""

 # Copy and setup entrypoint
-COPY image_support_files/docker-entrypoint.sh /usr/local/bin/docker-entrypoint.sh
+COPY image_support_files/minicpm45v_entrypoint.sh /usr/local/bin/docker-entrypoint.sh
 RUN chmod +x /usr/local/bin/docker-entrypoint.sh

 # Expose Ollama API port
--- a/2
+++ b/2
@@ -12,7 +12,7 @@ ENV OLLAMA_HOST="0.0.0.0"
 ENV OLLAMA_ORIGINS="*"

 # Copy and setup entrypoint
-COPY image_support_files/docker-entrypoint.sh /usr/local/bin/docker-entrypoint.sh
+COPY image_support_files/minicpm45v_entrypoint.sh /usr/local/bin/docker-entrypoint.sh
 RUN chmod +x /usr/local/bin/docker-entrypoint.sh

 # Expose Ollama API port
--- a/70
+++ b/70
@@ -1,70 +0,0 @@
-# PaddleOCR-VL GPU Variant
-# Vision-Language Model for document parsing using vLLM
-FROM nvidia/cuda:12.4.0-devel-ubuntu22.04
-
-LABEL maintainer="Task Venture Capital GmbH <hello@task.vc>"
-LABEL description="PaddleOCR-VL 0.9B - Vision-Language Model for document parsing"
-LABEL org.opencontainers.image.source="https://code.foss.global/host.today/ht-docker-ai"
-
-# Environment configuration
-ENV DEBIAN_FRONTEND=noninteractive
-ENV PYTHONUNBUFFERED=1
-ENV HF_HOME=/root/.cache/huggingface
-ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
-
-# Set working directory
-WORKDIR /app
-
-# Install system dependencies
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    python3.11 \
-    python3.11-venv \
-    python3.11-dev \
-    python3-pip \
-    git \
-    curl \
-    build-essential \
-    && rm -rf /var/lib/apt/lists/* \
-    && update-alternatives --install /usr/bin/python python /usr/bin/python3.11 1 \
-    && update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1
-
-# Create and activate virtual environment
-RUN python -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
-
-# Install PyTorch with CUDA support
-RUN pip install --no-cache-dir --upgrade pip && \
-    pip install --no-cache-dir \
-    torch==2.5.1 \
-    torchvision \
-    --index-url https://download.pytorch.org/whl/cu124
-
-# Install vLLM 0.11.1 (first stable release with PaddleOCR-VL support)
-RUN pip install --no-cache-dir \
-    vllm==0.11.1 \
-    --extra-index-url https://download.pytorch.org/whl/cu124
-
-# Install additional dependencies
-RUN pip install --no-cache-dir \
-    transformers \
-    accelerate \
-    safetensors \
-    pillow \
-    fastapi \
-    uvicorn[standard] \
-    python-multipart \
-    openai \
-    httpx
-
-# Copy entrypoint script
-COPY image_support_files/paddleocr-vl-entrypoint.sh /usr/local/bin/paddleocr-vl-entrypoint.sh
-RUN chmod +x /usr/local/bin/paddleocr-vl-entrypoint.sh
-
-# Expose vLLM API port
-EXPOSE 8000
-
-# Health check
-HEALTHCHECK --interval=30s --timeout=10s --start-period=300s --retries=3 \
-    CMD curl -f http://localhost:8000/health || exit 1
-
-ENTRYPOINT ["/usr/local/bin/paddleocr-vl-entrypoint.sh"]
--- a/2
+++ b/2
@@ -44,7 +44,7 @@ RUN pip install --no-cache-dir --upgrade pip && \

 # Copy server files
 COPY image_support_files/paddleocr_vl_server.py /app/paddleocr_vl_server.py
-COPY image_support_files/paddleocr-vl-cpu-entrypoint.sh /usr/local/bin/paddleocr-vl-cpu-entrypoint.sh
+COPY image_support_files/paddleocr_vl_entrypoint.sh /usr/local/bin/paddleocr-vl-cpu-entrypoint.sh
 RUN chmod +x /usr/local/bin/paddleocr-vl-cpu-entrypoint.sh

 # Expose API port
--- a/90
+++ b/90
@@ -0,0 +1,90 @@
+# PaddleOCR-VL Full Pipeline (PP-DocLayoutV2 + PaddleOCR-VL + Structured Output)
+# Self-contained GPU image with complete document parsing pipeline
+FROM nvidia/cuda:12.4.0-devel-ubuntu22.04
+
+LABEL maintainer="Task Venture Capital GmbH <hello@task.vc>"
+LABEL description="PaddleOCR-VL Full Pipeline - Layout Detection + VL Recognition + JSON/Markdown Output"
+LABEL org.opencontainers.image.source="https://code.foss.global/host.today/ht-docker-ai"
+
+# Environment configuration
+ENV DEBIAN_FRONTEND=noninteractive
+ENV PYTHONUNBUFFERED=1
+ENV HF_HOME=/root/.cache/huggingface
+ENV PADDLEOCR_HOME=/root/.paddleocr
+ENV SERVER_PORT=8000
+ENV SERVER_HOST=0.0.0.0
+ENV VLM_PORT=8080
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    python3.11 \
+    python3.11-venv \
+    python3.11-dev \
+    python3-pip \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    libgomp1 \
+    libsm6 \
+    libxext6 \
+    libxrender1 \
+    curl \
+    git \
+    wget \
+    && rm -rf /var/lib/apt/lists/* \
+    && update-alternatives --install /usr/bin/python python /usr/bin/python3.11 1 \
+    && update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1
+
+# Create and activate virtual environment
+RUN python -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# Upgrade pip
+RUN pip install --no-cache-dir --upgrade pip setuptools wheel
+
+# Install PaddlePaddle GPU (CUDA 12.x)
+RUN pip install --no-cache-dir \
+    paddlepaddle-gpu==3.2.1 \
+    --extra-index-url https://www.paddlepaddle.org.cn/packages/stable/cu126/
+
+# Install PaddleOCR with doc-parser (includes PP-DocLayoutV2)
+RUN pip install --no-cache-dir \
+    "paddleocr[doc-parser]" \
+    safetensors
+
+# Install PyTorch with CUDA support
+RUN pip install --no-cache-dir \
+    torch==2.5.1 \
+    torchvision \
+    --index-url https://download.pytorch.org/whl/cu124
+
+# Install transformers for PaddleOCR-VL inference (no vLLM - use local inference)
+# PaddleOCR-VL requires transformers>=4.55.0 for use_kernel_forward_from_hub
+RUN pip install --no-cache-dir \
+    transformers>=4.55.0 \
+    accelerate \
+    hf-kernels
+
+# Install our API server dependencies
+RUN pip install --no-cache-dir \
+    fastapi \
+    uvicorn[standard] \
+    python-multipart \
+    httpx \
+    pillow
+
+# Copy server files
+COPY image_support_files/paddleocr_vl_full_server.py /app/server.py
+COPY image_support_files/paddleocr_vl_full_entrypoint.sh /usr/local/bin/entrypoint.sh
+RUN chmod +x /usr/local/bin/entrypoint.sh
+
+# Expose ports (8000 = API, 8080 = internal VLM server)
+EXPOSE 8000
+
+# Health check
+HEALTHCHECK --interval=30s --timeout=10s --start-period=600s --retries=3 \
+    CMD curl -f http://localhost:8000/health || exit 1
+
+ENTRYPOINT ["/usr/local/bin/entrypoint.sh"]
--- a/2
+++ b/2
@@ -58,7 +58,7 @@ RUN pip install --no-cache-dir \

 # Copy server files (same as CPU variant - it auto-detects CUDA)
 COPY image_support_files/paddleocr_vl_server.py /app/paddleocr_vl_server.py
-COPY image_support_files/paddleocr-vl-cpu-entrypoint.sh /usr/local/bin/paddleocr-vl-entrypoint.sh
+COPY image_support_files/paddleocr_vl_entrypoint.sh /usr/local/bin/paddleocr-vl-entrypoint.sh
 RUN chmod +x /usr/local/bin/paddleocr-vl-entrypoint.sh

 # Expose API port
--- a/build-images.sh
+++ b/build-images.sh
@@ -16,7 +16,7 @@ echo -e "${BLUE}Building ht-docker-ai images...${NC}"
 # Build GPU variant
 echo -e "${GREEN}Building MiniCPM-V 4.5 GPU variant...${NC}"
 docker build \
-    -f Dockerfile_minicpm45v \
+    -f Dockerfile_minicpm45v_gpu \
    -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:minicpm45v \
    -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:minicpm45v-gpu \
    -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:latest \
@@ -29,10 +29,10 @@ docker build \
    -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:minicpm45v-cpu \
    .

-# Build PaddleOCR-VL GPU variant (vLLM)
-echo -e "${GREEN}Building PaddleOCR-VL GPU variant (vLLM)...${NC}"
+# Build PaddleOCR-VL GPU variant
+echo -e "${GREEN}Building PaddleOCR-VL GPU variant...${NC}"
 docker build \
-    -f Dockerfile_paddleocr_vl \
+    -f Dockerfile_paddleocr_vl_gpu \
    -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:paddleocr-vl \
    -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:paddleocr-vl-gpu \
    .
--- a/changelog.md
+++ b/changelog.md
@@ -1,5 +1,42 @@
 # Changelog

+## 2026-01-18 - 1.8.0 - feat(paddleocr-vl)
+add structured HTML output and table parsing for PaddleOCR-VL, update API, tests, and README
+
+- Add result_to_html(), parse_markdown_table(), and parse_paddleocr_table() to emit semantic HTML and convert OCR/markdown tables to proper <table> elements
+- Enhance result_to_markdown() with positional/type hints (header/footer/title/table/figure) to improve downstream LLM processing
+- Expose 'html' in supported formats and handle output_format='html' in parse endpoints and CLI flow
+- Update tests to request HTML output and extract invoice fields from structured HTML (test/test.invoices.paddleocr-vl.ts)
+- Refresh README with usage, new images/tags, architecture notes, and troubleshooting for the updated pipeline
+
+## 2026-01-17 - 1.7.1 - fix(docker)
+standardize Dockerfile and entrypoint filenames; add GPU-specific Dockerfiles and update build and test references
+
+- Added Dockerfile_minicpm45v_gpu and image_support_files/minicpm45v_entrypoint.sh; removed the old Dockerfile_minicpm45v and docker-entrypoint.sh
+- Renamed and simplified PaddleOCR entrypoint to image_support_files/paddleocr_vl_entrypoint.sh and updated CPU/GPU Dockerfile references
+- Updated build-images.sh to use *_gpu Dockerfiles and clarified PaddleOCR GPU build log
+- Updated test/helpers/docker.ts to point to Dockerfile_minicpm45v_gpu so tests build the GPU variant
+
+## 2026-01-17 - 1.7.0 - feat(tests)
+use Qwen2.5 (Ollama) for invoice extraction tests and add helpers for model management; normalize dates and coerce numeric fields
+
+- Added ensureOllamaModel and ensureQwen25 test helpers to pull/check Ollama models via localhost:11434
+- Updated invoices test to use qwen2.5:7b instead of MiniCPM and removed image payload from the text-only extraction step
+- Increased Markdown truncate limit from 8000 to 12000 and reduced model num_predict from 2048 to 512
+- Rewrote extraction prompt to require strict JSON output and added post-processing to parse/convert numeric fields
+- Added normalizeDate and improved compareInvoice to normalize dates and handle numeric formatting/tolerance
+- Updated test setup to ensure Qwen2.5 is available and adjusted logging/messages to reflect the Qwen2.5-based workflow
+
+## 2026-01-17 - 1.6.0 - feat(paddleocr-vl)
+add PaddleOCR-VL full pipeline Docker image and API server, plus integration tests and docker helpers
+
+- Add Dockerfile_paddleocr_vl_full and entrypoint script to build a GPU-enabled image with PP-DocLayoutV2 + PaddleOCR-VL and a FastAPI server
+- Introduce image_support_files/paddleocr_vl_full_server.py implementing the full pipeline API (/parse, OpenAI-compatible /v1/chat/completions) and a /formats endpoint
+- Improve image handling: decode_image supports data URLs, HTTP(S), raw base64 and file paths; add optimize_image_resolution to auto-scale images into the recommended 1080-2048px range
+- Add test helpers (test/helpers/docker.ts) to build/start/health-check Docker images and new ensurePaddleOcrVlFull workflow
+- Add comprehensive integration tests for bank statements and invoices (MiniCPM and PaddleOCR-VL variants) and update tests to ensure required containers are running before tests
+- Switch MiniCPM model references to 'minicpm-v:latest' and increase health/timeout expectations for the full pipeline
+
 ## 2026-01-17 - 1.5.0 - feat(paddleocr-vl)
 add PaddleOCR-VL GPU Dockerfile, pin vllm, update CPU image deps, and improve entrypoint and tests

--- a/image_support_files/minicpm45v_entrypoint.sh
+++ b/image_support_files/minicpm45v_entrypoint.sh
--- a/image_support_files/paddleocr-vl-entrypoint.sh
+++ b/image_support_files/paddleocr-vl-entrypoint.sh
@@ -1,59 +0,0 @@
-#!/bin/bash
-set -e
-
-echo "==================================="
-echo "PaddleOCR-VL Server"
-echo "==================================="
-
-# Configuration
-MODEL_NAME="${MODEL_NAME:-PaddlePaddle/PaddleOCR-VL}"
-HOST="${HOST:-0.0.0.0}"
-PORT="${PORT:-8000}"
-MAX_BATCHED_TOKENS="${MAX_BATCHED_TOKENS:-16384}"
-GPU_MEMORY_UTILIZATION="${GPU_MEMORY_UTILIZATION:-0.9}"
-MAX_MODEL_LEN="${MAX_MODEL_LEN:-8192}"
-ENFORCE_EAGER="${ENFORCE_EAGER:-false}"
-
-echo "Model: ${MODEL_NAME}"
-echo "Host: ${HOST}"
-echo "Port: ${PORT}"
-echo "Max batched tokens: ${MAX_BATCHED_TOKENS}"
-echo "GPU memory utilization: ${GPU_MEMORY_UTILIZATION}"
-echo "Max model length: ${MAX_MODEL_LEN}"
-echo "Enforce eager: ${ENFORCE_EAGER}"
-echo ""
-
-# Check GPU availability
-if command -v nvidia-smi &> /dev/null; then
-    echo "GPU Information:"
-    nvidia-smi --query-gpu=name,memory.total,memory.free --format=csv
-    echo ""
-else
-    echo "WARNING: nvidia-smi not found. GPU may not be available."
-fi
-
-echo "Starting vLLM server..."
-echo "==================================="
-
-# Build vLLM command
-VLLM_ARGS=(
-    serve "${MODEL_NAME}"
-    --trust-remote-code
-    --host "${HOST}"
-    --port "${PORT}"
-    --max-num-batched-tokens "${MAX_BATCHED_TOKENS}"
-    --gpu-memory-utilization "${GPU_MEMORY_UTILIZATION}"
-    --max-model-len "${MAX_MODEL_LEN}"
-    --no-enable-prefix-caching
-    --mm-processor-cache-gb 0
-    --served-model-name "paddleocr-vl"
-    --limit-mm-per-prompt '{"image": 1}'
-)
-
-# Add enforce-eager if enabled (disables CUDA graphs, saves memory)
-if [ "${ENFORCE_EAGER}" = "true" ]; then
-    VLLM_ARGS+=(--enforce-eager)
-fi
-
-# Start vLLM server with PaddleOCR-VL
-exec vllm "${VLLM_ARGS[@]}"
--- a/image_support_files/paddleocr-vl-cpu-entrypoint.sh
+++ b/image_support_files/paddleocr-vl-cpu-entrypoint.sh
--- a/image_support_files/paddleocr_vl_full_entrypoint.sh
+++ b/image_support_files/paddleocr_vl_full_entrypoint.sh
@@ -0,0 +1,12 @@
+#!/bin/bash
+set -e
+
+echo "Starting PaddleOCR-VL Full Pipeline Server (Transformers backend)..."
+
+# Environment
+SERVER_PORT=${SERVER_PORT:-8000}
+SERVER_HOST=${SERVER_HOST:-0.0.0.0}
+
+# Start our API server directly (no vLLM - uses local transformers inference)
+echo "Starting API server on port $SERVER_PORT..."
+exec python /app/server.py
--- a/image_support_files/paddleocr_vl_full_server.py
+++ b/image_support_files/paddleocr_vl_full_server.py
@@ -0,0 +1,636 @@
+#!/usr/bin/env python3
+"""
+PaddleOCR-VL Full Pipeline API Server (Transformers backend)
+
+Provides REST API for document parsing using:
+- PP-DocLayoutV2 for layout detection
+- PaddleOCR-VL (transformers) for recognition
+- Structured JSON/Markdown output
+"""
+
+import os
+import io
+import re
+import base64
+import logging
+import tempfile
+import time
+import json
+from typing import Optional, List, Union
+from pathlib import Path
+
+from fastapi import FastAPI, HTTPException, UploadFile, File, Form
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel
+from PIL import Image
+import torch
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+# Environment configuration
+SERVER_HOST = os.environ.get('SERVER_HOST', '0.0.0.0')
+SERVER_PORT = int(os.environ.get('SERVER_PORT', '8000'))
+MODEL_NAME = "PaddlePaddle/PaddleOCR-VL"
+
+# Device configuration
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+logger.info(f"Using device: {DEVICE}")
+
+# Task prompts
+TASK_PROMPTS = {
+    "ocr": "OCR:",
+    "table": "Table Recognition:",
+    "formula": "Formula Recognition:",
+    "chart": "Chart Recognition:",
+}
+
+# Initialize FastAPI app
+app = FastAPI(
+    title="PaddleOCR-VL Full Pipeline Server",
+    description="Document parsing with PP-DocLayoutV2 + PaddleOCR-VL (transformers)",
+    version="1.0.0"
+)
+
+# Global model instances
+vl_model = None
+vl_processor = None
+layout_model = None
+
+
+def load_vl_model():
+    """Load the PaddleOCR-VL model for element recognition"""
+    global vl_model, vl_processor
+
+    if vl_model is not None:
+        return
+
+    logger.info(f"Loading PaddleOCR-VL model: {MODEL_NAME}")
+    from transformers import AutoModelForCausalLM, AutoProcessor
+
+    vl_processor = AutoProcessor.from_pretrained(MODEL_NAME, trust_remote_code=True)
+
+    if DEVICE == "cuda":
+        vl_model = AutoModelForCausalLM.from_pretrained(
+            MODEL_NAME,
+            trust_remote_code=True,
+            torch_dtype=torch.bfloat16,
+        ).to(DEVICE).eval()
+    else:
+        vl_model = AutoModelForCausalLM.from_pretrained(
+            MODEL_NAME,
+            trust_remote_code=True,
+            torch_dtype=torch.float32,
+            low_cpu_mem_usage=True,
+        ).eval()
+
+    logger.info("PaddleOCR-VL model loaded successfully")
+
+
+def load_layout_model():
+    """Load the LayoutDetection model for layout detection"""
+    global layout_model
+
+    if layout_model is not None:
+        return
+
+    try:
+        logger.info("Loading LayoutDetection model (PP-DocLayout_plus-L)...")
+        from paddleocr import LayoutDetection
+
+        layout_model = LayoutDetection()
+        logger.info("LayoutDetection model loaded successfully")
+    except Exception as e:
+        logger.warning(f"Could not load LayoutDetection: {e}")
+        logger.info("Falling back to VL-only mode (no layout detection)")
+
+
+def recognize_element(image: Image.Image, task: str = "ocr") -> str:
+    """Recognize a single element using PaddleOCR-VL"""
+    load_vl_model()
+
+    prompt = TASK_PROMPTS.get(task, TASK_PROMPTS["ocr"])
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "image": image},
+                {"type": "text", "text": prompt},
+            ]
+        }
+    ]
+
+    inputs = vl_processor.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_dict=True,
+        return_tensors="pt"
+    )
+
+    if DEVICE == "cuda":
+        inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
+
+    with torch.inference_mode():
+        outputs = vl_model.generate(
+            **inputs,
+            max_new_tokens=4096,
+            do_sample=False,
+            use_cache=True
+        )
+
+    response = vl_processor.batch_decode(outputs, skip_special_tokens=True)[0]
+
+    # Extract only the assistant's response content
+    # The response format is: "User: <prompt>\nAssistant: <content>"
+    # We want to extract just the content after "Assistant:"
+    if "Assistant:" in response:
+        parts = response.split("Assistant:")
+        if len(parts) > 1:
+            response = parts[-1].strip()
+    elif "assistant:" in response.lower():
+        # Case-insensitive fallback
+        import re
+        match = re.split(r'[Aa]ssistant:', response)
+        if len(match) > 1:
+            response = match[-1].strip()
+
+    return response
+
+
+def detect_layout(image: Image.Image) -> List[dict]:
+    """Detect layout regions in the image"""
+    load_layout_model()
+
+    if layout_model is None:
+        # No layout model - return a single region covering the whole image
+        return [{
+            "type": "text",
+            "bbox": [0, 0, image.width, image.height],
+            "score": 1.0
+        }]
+
+    # Save image to temp file
+    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
+        image.save(tmp.name, "PNG")
+        tmp_path = tmp.name
+
+    try:
+        results = layout_model.predict(tmp_path)
+        regions = []
+
+        for res in results:
+            # LayoutDetection returns boxes in 'boxes' key
+            for box in res.get("boxes", []):
+                coord = box.get("coordinate", [0, 0, image.width, image.height])
+                # Convert numpy floats to regular floats
+                bbox = [float(c) for c in coord]
+                regions.append({
+                    "type": box.get("label", "text"),
+                    "bbox": bbox,
+                    "score": float(box.get("score", 1.0))
+                })
+
+        # Sort regions by vertical position (top to bottom)
+        regions.sort(key=lambda r: r["bbox"][1])
+
+        return regions if regions else [{
+            "type": "text",
+            "bbox": [0, 0, image.width, image.height],
+            "score": 1.0
+        }]
+
+    finally:
+        os.unlink(tmp_path)
+
+
+def process_document(image: Image.Image) -> dict:
+    """Process a document through the full pipeline"""
+    logger.info(f"Processing document: {image.size}")
+
+    # Step 1: Detect layout
+    regions = detect_layout(image)
+    logger.info(f"Detected {len(regions)} layout regions")
+
+    # Step 2: Recognize each region
+    blocks = []
+    for i, region in enumerate(regions):
+        region_type = region["type"].lower()
+        bbox = region["bbox"]
+
+        # Crop region from image
+        x1, y1, x2, y2 = [int(c) for c in bbox]
+        region_image = image.crop((x1, y1, x2, y2))
+
+        # Determine task based on region type
+        if "table" in region_type:
+            task = "table"
+        elif "formula" in region_type or "math" in region_type:
+            task = "formula"
+        elif "chart" in region_type or "figure" in region_type:
+            task = "chart"
+        else:
+            task = "ocr"
+
+        # Recognize the region
+        try:
+            content = recognize_element(region_image, task)
+            blocks.append({
+                "index": i,
+                "type": region_type,
+                "bbox": bbox,
+                "content": content,
+                "task": task
+            })
+            logger.info(f"  Region {i} ({region_type}): {len(content)} chars")
+        except Exception as e:
+            logger.error(f"  Region {i} error: {e}")
+            blocks.append({
+                "index": i,
+                "type": region_type,
+                "bbox": bbox,
+                "content": "",
+                "error": str(e)
+            })
+
+    return {"blocks": blocks, "image_size": list(image.size)}
+
+
+def result_to_markdown(result: dict) -> str:
+    """Convert result to Markdown format with structural hints for LLM processing.
+
+    Adds positional and type-based formatting to help downstream LLMs
+    understand document structure:
+    - Tables are marked with **[TABLE]** prefix
+    - Header zone content (top 15%) is bolded
+    - Footer zone content (bottom 15%) is separated with horizontal rule
+    - Titles are formatted as # headers
+    - Figures/charts are marked with *[Figure: ...]*
+    """
+    lines = []
+    image_height = result.get("image_size", [0, 1000])[1]
+
+    for block in result.get("blocks", []):
+        block_type = block.get("type", "text").lower()
+        content = block.get("content", "").strip()
+        bbox = block.get("bbox", [])
+
+        if not content:
+            continue
+
+        # Determine position zone (top 15%, middle, bottom 15%)
+        y_pos = bbox[1] if bbox and len(bbox) > 1 else 0
+        y_end = bbox[3] if bbox and len(bbox) > 3 else y_pos
+        is_header_zone = y_pos < image_height * 0.15
+        is_footer_zone = y_end > image_height * 0.85
+
+        # Format based on type and position
+        if "table" in block_type:
+            lines.append(f"\n**[TABLE]**\n{content}\n")
+        elif "title" in block_type:
+            lines.append(f"# {content}")
+        elif "formula" in block_type or "math" in block_type:
+            lines.append(f"\n$$\n{content}\n$$\n")
+        elif "figure" in block_type or "chart" in block_type:
+            lines.append(f"*[Figure: {content}]*")
+        elif is_header_zone:
+            lines.append(f"**{content}**")
+        elif is_footer_zone:
+            lines.append(f"---\n{content}")
+        else:
+            lines.append(content)
+
+    return "\n\n".join(lines)
+
+
+def parse_markdown_table(content: str) -> str:
+    """Convert table content to HTML table.
+
+    Handles:
+    - PaddleOCR-VL format: <fcel>cell<lcel>cell<nl> (detected by <fcel> tags)
+    - Pipe-delimited tables: | Header | Header |
+    - Separator rows: |---|---|
+    - Returns HTML <table> structure
+    """
+    content_stripped = content.strip()
+
+    # Check for PaddleOCR-VL table format (<fcel>, <lcel>, <ecel>, <nl>)
+    if '<fcel>' in content_stripped or '<nl>' in content_stripped:
+        return parse_paddleocr_table(content_stripped)
+
+    lines = content_stripped.split('\n')
+    if not lines:
+        return f'<pre>{content}</pre>'
+
+    # Check if it looks like a markdown table
+    if not any('|' in line for line in lines):
+        return f'<pre>{content}</pre>'
+
+    html_rows = []
+    is_header = True
+
+    for line in lines:
+        line = line.strip()
+        if not line or line.startswith('|') == False and '|' not in line:
+            continue
+
+        # Skip separator rows (|---|---|)
+        if re.match(r'^[\|\s\-:]+$', line):
+            is_header = False
+            continue
+
+        # Parse cells
+        cells = [c.strip() for c in line.split('|')]
+        cells = [c for c in cells if c]  # Remove empty from edges
+
+        if is_header:
+            row = '<tr>' + ''.join(f'<th>{c}</th>' for c in cells) + '</tr>'
+            html_rows.append(f'<thead>{row}</thead>')
+            is_header = False
+        else:
+            row = '<tr>' + ''.join(f'<td>{c}</td>' for c in cells) + '</tr>'
+            html_rows.append(row)
+
+    if html_rows:
+        # Wrap body rows in tbody
+        header = html_rows[0] if '<thead>' in html_rows[0] else ''
+        body_rows = [r for r in html_rows if '<thead>' not in r]
+        body = f'<tbody>{"".join(body_rows)}</tbody>' if body_rows else ''
+        return f'<table>{header}{body}</table>'
+
+    return f'<pre>{content}</pre>'
+
+
+def parse_paddleocr_table(content: str) -> str:
+    """Convert PaddleOCR-VL table format to HTML table.
+
+    PaddleOCR-VL uses:
+    - <fcel> = first cell in a row
+    - <lcel> = subsequent cells
+    - <ecel> = empty cell
+    - <nl> = row separator (newline)
+
+    Example input:
+    <fcel>Header1<lcel>Header2<nl><fcel>Value1<lcel>Value2<nl>
+    """
+    # Split into rows by <nl>
+    rows_raw = re.split(r'<nl>', content)
+    html_rows = []
+    is_first_row = True
+
+    for row_content in rows_raw:
+        row_content = row_content.strip()
+        if not row_content:
+            continue
+
+        # Extract cells: split by <fcel>, <lcel>, or <ecel>
+        # Each cell is the text between these markers
+        cells = []
+
+        # Pattern to match cell markers and capture content
+        # Content is everything between markers
+        parts = re.split(r'<fcel>|<lcel>|<ecel>', row_content)
+        for part in parts:
+            part = part.strip()
+            if part:
+                cells.append(part)
+
+        if not cells:
+            continue
+
+        # First row is header
+        if is_first_row:
+            row_html = '<tr>' + ''.join(f'<th>{c}</th>' for c in cells) + '</tr>'
+            html_rows.append(f'<thead>{row_html}</thead>')
+            is_first_row = False
+        else:
+            row_html = '<tr>' + ''.join(f'<td>{c}</td>' for c in cells) + '</tr>'
+            html_rows.append(row_html)
+
+    if html_rows:
+        header = html_rows[0] if '<thead>' in html_rows[0] else ''
+        body_rows = [r for r in html_rows if '<thead>' not in r]
+        body = f'<tbody>{"".join(body_rows)}</tbody>' if body_rows else ''
+        return f'<table>{header}{body}</table>'
+
+    return f'<pre>{content}</pre>'
+
+
+def result_to_html(result: dict) -> str:
+    """Convert result to semantic HTML for optimal LLM processing.
+
+    Uses semantic HTML5 tags with position metadata as data-* attributes.
+    Markdown tables are converted to proper HTML <table> tags for
+    unambiguous parsing by downstream LLMs.
+    """
+    parts = []
+    image_height = result.get("image_size", [0, 1000])[1]
+
+    parts.append('<!DOCTYPE html><html><body>')
+
+    for block in result.get("blocks", []):
+        block_type = block.get("type", "text").lower()
+        content = block.get("content", "").strip()
+        bbox = block.get("bbox", [])
+
+        if not content:
+            continue
+
+        # Position metadata
+        y_pos = bbox[1] / image_height if bbox and len(bbox) > 1 else 0
+        data_attrs = f'data-type="{block_type}" data-y="{y_pos:.2f}"'
+
+        # Format based on type
+        if "table" in block_type:
+            table_html = parse_markdown_table(content)
+            parts.append(f'<section {data_attrs} class="table-region">{table_html}</section>')
+        elif "title" in block_type:
+            parts.append(f'<h1 {data_attrs}>{content}</h1>')
+        elif "formula" in block_type or "math" in block_type:
+            parts.append(f'<div {data_attrs} class="formula"><code>{content}</code></div>')
+        elif "figure" in block_type or "chart" in block_type:
+            parts.append(f'<figure {data_attrs}><figcaption>{content}</figcaption></figure>')
+        elif y_pos < 0.15:
+            parts.append(f'<header {data_attrs}><strong>{content}</strong></header>')
+        elif y_pos > 0.85:
+            parts.append(f'<footer {data_attrs}>{content}</footer>')
+        else:
+            parts.append(f'<p {data_attrs}>{content}</p>')
+
+    parts.append('</body></html>')
+    return '\n'.join(parts)
+
+
+# Request/Response models
+class ParseRequest(BaseModel):
+    image: str  # base64 encoded image
+    output_format: Optional[str] = "json"
+
+
+class ParseResponse(BaseModel):
+    success: bool
+    format: str
+    result: Union[dict, str]
+    processing_time: float
+    error: Optional[str] = None
+
+
+def decode_image(image_source: str) -> Image.Image:
+    """Decode image from base64 or data URL"""
+    if image_source.startswith("data:"):
+        header, data = image_source.split(",", 1)
+        image_data = base64.b64decode(data)
+    else:
+        image_data = base64.b64decode(image_source)
+
+    return Image.open(io.BytesIO(image_data)).convert("RGB")
+
+
+@app.on_event("startup")
+async def startup_event():
+    """Pre-load models on startup"""
+    logger.info("Starting PaddleOCR-VL Full Pipeline Server...")
+    try:
+        load_vl_model()
+        load_layout_model()
+        logger.info("Models loaded successfully")
+    except Exception as e:
+        logger.error(f"Failed to pre-load models: {e}")
+
+
+@app.get("/health")
+async def health_check():
+    """Health check endpoint"""
+    return {
+        "status": "healthy" if vl_model is not None else "loading",
+        "service": "PaddleOCR-VL Full Pipeline (Transformers)",
+        "device": DEVICE,
+        "vl_model_loaded": vl_model is not None,
+        "layout_model_loaded": layout_model is not None
+    }
+
+
+@app.get("/formats")
+async def supported_formats():
+    """List supported output formats"""
+    return {
+        "output_formats": ["json", "markdown", "html"],
+        "image_formats": ["PNG", "JPEG", "WebP", "BMP", "GIF", "TIFF"],
+        "capabilities": [
+            "Layout detection (PP-DocLayoutV2)",
+            "Text recognition (OCR)",
+            "Table recognition",
+            "Formula recognition (LaTeX)",
+            "Chart recognition",
+            "Multi-language support (109 languages)"
+        ]
+    }
+
+
+@app.post("/parse", response_model=ParseResponse)
+async def parse_document_endpoint(request: ParseRequest):
+    """Parse a document image and return structured output"""
+    try:
+        start_time = time.time()
+
+        image = decode_image(request.image)
+        result = process_document(image)
+
+        if request.output_format == "markdown":
+            markdown = result_to_markdown(result)
+            output = {"markdown": markdown}
+        elif request.output_format == "html":
+            html = result_to_html(result)
+            output = {"html": html}
+        else:
+            output = result
+
+        elapsed = time.time() - start_time
+        logger.info(f"Processing complete in {elapsed:.2f}s")
+
+        return ParseResponse(
+            success=True,
+            format=request.output_format,
+            result=output,
+            processing_time=elapsed
+        )
+
+    except Exception as e:
+        logger.error(f"Error processing document: {e}", exc_info=True)
+        return ParseResponse(
+            success=False,
+            format=request.output_format,
+            result={},
+            processing_time=0,
+            error=str(e)
+        )
+
+
+@app.post("/v1/chat/completions")
+async def chat_completions(request: dict):
+    """OpenAI-compatible chat completions endpoint"""
+    try:
+        messages = request.get("messages", [])
+        output_format = request.get("output_format", "json")
+
+        # Find user message with image
+        image = None
+        for msg in reversed(messages):
+            if msg.get("role") == "user":
+                content = msg.get("content", [])
+                if isinstance(content, list):
+                    for item in content:
+                        if item.get("type") == "image_url":
+                            url = item.get("image_url", {}).get("url", "")
+                            image = decode_image(url)
+                            break
+                break
+
+        if image is None:
+            raise HTTPException(status_code=400, detail="No image provided")
+
+        start_time = time.time()
+        result = process_document(image)
+
+        if output_format == "markdown":
+            content = result_to_markdown(result)
+        elif output_format == "html":
+            content = result_to_html(result)
+        else:
+            content = json.dumps(result, ensure_ascii=False, indent=2)
+
+        elapsed = time.time() - start_time
+
+        return {
+            "id": f"chatcmpl-{int(time.time()*1000)}",
+            "object": "chat.completion",
+            "created": int(time.time()),
+            "model": "paddleocr-vl-full",
+            "choices": [{
+                "index": 0,
+                "message": {"role": "assistant", "content": content},
+                "finish_reason": "stop"
+            }],
+            "usage": {
+                "prompt_tokens": 100,
+                "completion_tokens": len(content) // 4,
+                "total_tokens": 100 + len(content) // 4
+            },
+            "processing_time": elapsed
+        }
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error in chat completions: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host=SERVER_HOST, port=SERVER_PORT)
--- a/image_support_files/paddleocr_vl_server.py
+++ b/image_support_files/paddleocr_vl_server.py
@@ -136,27 +136,82 @@ def load_model():
    logger.info("PaddleOCR-VL model loaded successfully")


-def decode_image(image_source: str) -> Image.Image:
-    """Decode image from URL or base64"""
+def optimize_image_resolution(image: Image.Image, max_size: int = 2048, min_size: int = 1080) -> Image.Image:
+    """
+    Optimize image resolution for PaddleOCR-VL.
+
+    Best results are achieved with images in the 1080p-2K range.
+    - Images larger than max_size are scaled down
+    - Very small images are scaled up to min_size
+    """
+    width, height = image.size
+    max_dim = max(width, height)
+    min_dim = min(width, height)
+
+    # Scale down if too large (4K+ images often miss text)
+    if max_dim > max_size:
+        scale = max_size / max_dim
+        new_width = int(width * scale)
+        new_height = int(height * scale)
+        logger.info(f"Scaling down image from {width}x{height} to {new_width}x{new_height}")
+        image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
+    # Scale up if too small
+    elif max_dim < min_size and min_dim < min_size:
+        scale = min_size / max_dim
+        new_width = int(width * scale)
+        new_height = int(height * scale)
+        logger.info(f"Scaling up image from {width}x{height} to {new_width}x{new_height}")
+        image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
+    else:
+        logger.info(f"Image size {width}x{height} is optimal, no scaling needed")
+
+    return image
+
+
+def decode_image(image_source: str, optimize: bool = True) -> Image.Image:
+    """
+    Decode image from various sources.
+
+    Supported formats:
+    - Base64 data URL: data:image/png;base64,... or data:image/jpeg;base64,...
+    - HTTP/HTTPS URL: https://example.com/image.png
+    - Raw base64 string
+    - Local file path
+
+    Supported image types: PNG, JPEG, WebP, BMP, GIF, TIFF
+    """
+    image = None
+
    if image_source.startswith("data:"):
-        # Base64 encoded image
+        # Base64 encoded image with MIME type header
+        # Supports: data:image/png;base64,... data:image/jpeg;base64,... etc.
        header, data = image_source.split(",", 1)
        image_data = base64.b64decode(data)
-        return Image.open(io.BytesIO(image_data)).convert("RGB")
+        image = Image.open(io.BytesIO(image_data)).convert("RGB")
+        logger.debug(f"Decoded base64 image with header: {header}")
    elif image_source.startswith("http://") or image_source.startswith("https://"):
        # URL - fetch image
        import httpx
        response = httpx.get(image_source, timeout=30.0)
        response.raise_for_status()
-        return Image.open(io.BytesIO(response.content)).convert("RGB")
+        image = Image.open(io.BytesIO(response.content)).convert("RGB")
+        logger.debug(f"Fetched image from URL: {image_source[:50]}...")
    else:
        # Assume it's a file path or raw base64
        try:
            image_data = base64.b64decode(image_source)
-            return Image.open(io.BytesIO(image_data)).convert("RGB")
+            image = Image.open(io.BytesIO(image_data)).convert("RGB")
+            logger.debug("Decoded raw base64 image")
        except:
            # Try as file path
-            return Image.open(image_source).convert("RGB")
+            image = Image.open(image_source).convert("RGB")
+            logger.debug(f"Loaded image from file: {image_source}")
+
+    # Optimize resolution for best OCR results
+    if optimize:
+        image = optimize_image_resolution(image)
+
+    return image


 def extract_image_and_text(content: Union[str, List[ContentItem]]) -> tuple:
@@ -242,6 +297,45 @@ async def health_check():
    )


+@app.get("/formats")
+async def supported_formats():
+    """List supported image formats and input methods"""
+    return {
+        "image_formats": {
+            "supported": ["PNG", "JPEG", "WebP", "BMP", "GIF", "TIFF"],
+            "recommended": ["PNG", "JPEG"],
+            "mime_types": [
+                "image/png",
+                "image/jpeg",
+                "image/webp",
+                "image/bmp",
+                "image/gif",
+                "image/tiff"
+            ]
+        },
+        "input_methods": {
+            "base64_data_url": {
+                "description": "Base64 encoded image with MIME type header",
+                "example": "data:image/png;base64,iVBORw0KGgo..."
+            },
+            "http_url": {
+                "description": "Direct HTTP/HTTPS URL to image",
+                "example": "https://example.com/image.png"
+            },
+            "raw_base64": {
+                "description": "Raw base64 string without header",
+                "example": "iVBORw0KGgo..."
+            }
+        },
+        "resolution": {
+            "optimal_range": "1080p to 2K (1080-2048 pixels on longest side)",
+            "auto_scaling": True,
+            "note": "Images are automatically scaled to optimal range. 4K+ images are scaled down for better accuracy."
+        },
+        "task_prompts": TASK_PROMPTS
+    }
+
+
@app.get("/v1/models")
 async def list_models():
    """List available models (OpenAI-compatible)"""
--- a/package.json
+++ b/package.json
@@ -1,6 +1,6 @@
 {
  "name": "@host.today/ht-docker-ai",
-  "version": "1.5.0",
+  "version": "1.8.0",
  "type": "module",
  "private": false,
  "description": "Docker images for AI vision-language models including MiniCPM-V 4.5",
--- a/readme.md
+++ b/readme.md
@@ -1,23 +1,40 @@
-# @host.today/ht-docker-ai
+# @host.today/ht-docker-ai 🚀

-Docker images for AI vision-language models, starting with MiniCPM-V 4.5.
+Production-ready Docker images for state-of-the-art AI Vision-Language Models. Run powerful multimodal AI locally with GPU acceleration or CPU fallback—no cloud API keys required.

-## Overview
+## Issue Reporting and Security

-This project provides ready-to-use Docker containers for running state-of-the-art AI vision-language models. Built on Ollama for simplified model management and a consistent REST API.
+For reporting bugs, issues, or security vulnerabilities, please visit [community.foss.global/](https://community.foss.global/). This is the central community hub for all issue reporting. Developers who sign and comply with our contribution agreement and go through identification can also get a [code.foss.global/](https://code.foss.global/) account to submit Pull Requests directly.

-## Available Images
+## 🎯 What's Included

-| Tag | Description | Requirements |
-|-----|-------------|--------------|
-| `minicpm45v` | MiniCPM-V 4.5 with GPU support | NVIDIA GPU, 9-18GB VRAM |
-| `minicpm45v-cpu` | MiniCPM-V 4.5 CPU-only | 8GB+ RAM |
-| `latest` | Alias for `minicpm45v` | NVIDIA GPU |
+| Model | Parameters | Best For | API |
+|-------|-----------|----------|-----|
+| **MiniCPM-V 4.5** | 8B | General vision understanding, image analysis, multi-image | Ollama-compatible |
+| **PaddleOCR-VL** | 0.9B | Document parsing, table extraction, OCR | OpenAI-compatible |

-## Quick Start
+## 📦 Available Images

-### GPU (Recommended)
+```
+code.foss.global/host.today/ht-docker-ai:<tag>
+```

+| Tag | Model | Hardware | Port |
+|-----|-------|----------|------|
+| `minicpm45v` / `latest` | MiniCPM-V 4.5 | NVIDIA GPU (9-18GB VRAM) | 11434 |
+| `minicpm45v-cpu` | MiniCPM-V 4.5 | CPU only (8GB+ RAM) | 11434 |
+| `paddleocr-vl` / `paddleocr-vl-gpu` | PaddleOCR-VL | NVIDIA GPU | 8000 |
+| `paddleocr-vl-cpu` | PaddleOCR-VL | CPU only | 8000 |
+
+---
+
+## 🖼️ MiniCPM-V 4.5
+
+A GPT-4o level multimodal LLM from OpenBMB—handles image understanding, OCR, multi-image analysis, and visual reasoning across 30+ languages.
+
+### Quick Start
+
+**GPU (Recommended):**
 ```bash
 docker run -d \
  --name minicpm \
@@ -27,8 +44,7 @@ docker run -d \
  code.foss.global/host.today/ht-docker-ai:minicpm45v
 ```

-### CPU Only
-
+**CPU Only:**
 ```bash
 docker run -d \
  --name minicpm \
@@ -37,18 +53,16 @@ docker run -d \
  code.foss.global/host.today/ht-docker-ai:minicpm45v-cpu
 ```

-## API Usage
+> 💡 **Pro tip:** Mount the volume to persist downloaded models (~5GB). Without it, models re-download on every container start.

-The container exposes the Ollama API on port 11434.
-
-### List Available Models
+### API Examples

+**List models:**
 ```bash
 curl http://localhost:11434/api/tags
 ```

-### Generate Text from Image
-
+**Analyze an image:**
 ```bash
 curl http://localhost:11434/api/generate -d '{
  "model": "minicpm-v",
@@ -57,60 +71,128 @@ curl http://localhost:11434/api/generate -d '{
 }'
 ```

-### Chat with Vision
-
+**Chat with vision:**
 ```bash
 curl http://localhost:11434/api/chat -d '{
  "model": "minicpm-v",
-  "messages": [
-    {
+  "messages": [{
    "role": "user",
    "content": "Describe this image in detail",
    "images": ["<base64-encoded-image>"]
-    }
-  ]
+  }]
 }'
 ```

-## Environment Variables
+### Hardware Requirements

-| Variable | Default | Description |
-|----------|---------|-------------|
-| `MODEL_NAME` | `minicpm-v` | Model to pull on startup |
-| `OLLAMA_HOST` | `0.0.0.0` | Host address for API |
-| `OLLAMA_ORIGINS` | `*` | Allowed CORS origins |
+| Variant | VRAM/RAM | Notes |
+|---------|----------|-------|
+| GPU (int4 quantized) | 9GB VRAM | Recommended for most use cases |
+| GPU (full precision) | 18GB VRAM | Maximum quality |
+| CPU (GGUF) | 8GB+ RAM | Slower but accessible |

-## Hardware Requirements
+---

-### GPU Variant (`minicpm45v`)
+## 📄 PaddleOCR-VL

- NVIDIA GPU with CUDA support
- Minimum 9GB VRAM (int4 quantized)
- Recommended 18GB VRAM (full precision)
- NVIDIA Container Toolkit installed
+A specialized 0.9B Vision-Language Model optimized for document parsing. Native support for tables, formulas, charts, and text extraction in 109 languages.

-### CPU Variant (`minicpm45v-cpu`)
+### Quick Start

- Minimum 8GB RAM
- Recommended 16GB+ RAM for better performance
- No GPU required
+**GPU:**
+```bash
+docker run -d \
+  --name paddleocr \
+  --gpus all \
+  -p 8000:8000 \
+  -v hf-cache:/root/.cache/huggingface \
+  code.foss.global/host.today/ht-docker-ai:paddleocr-vl
+```

-## Model Information
+**CPU:**
+```bash
+docker run -d \
+  --name paddleocr \
+  -p 8000:8000 \
+  -v hf-cache:/root/.cache/huggingface \
+  code.foss.global/host.today/ht-docker-ai:paddleocr-vl-cpu
+```

-**MiniCPM-V 4.5** is a GPT-4o level multimodal large language model developed by OpenBMB.
+### OpenAI-Compatible API

- **Parameters**: 8B (Qwen3-8B + SigLIP2-400M)
- **Capabilities**: Image understanding, OCR, multi-image analysis
- **Languages**: 30+ languages including English, Chinese, French, Spanish
+PaddleOCR-VL exposes a fully OpenAI-compatible `/v1/chat/completions` endpoint:

-## Docker Compose Example
+```bash
+curl http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "paddleocr-vl",
+    "messages": [{
+      "role": "user",
+      "content": [
+        {"type": "image_url", "image_url": {"url": "data:image/png;base64,<base64>"}},
+        {"type": "text", "text": "Table Recognition:"}
+      ]
+    }],
+    "max_tokens": 8192
+  }'
+```
+
+### Task Prompts
+
+| Prompt | Output | Use Case |
+|--------|--------|----------|
+| `OCR:` | Plain text | General text extraction |
+| `Table Recognition:` | Markdown table | Invoices, bank statements, spreadsheets |
+| `Formula Recognition:` | LaTeX | Math equations, scientific notation |
+| `Chart Recognition:` | Description | Graphs and visualizations |
+
+### API Endpoints
+
+| Endpoint | Method | Description |
+|----------|--------|-------------|
+| `/health` | GET | Health check with model/device info |
+| `/formats` | GET | Supported image formats and input methods |
+| `/v1/models` | GET | List available models |
+| `/v1/chat/completions` | POST | OpenAI-compatible chat completions |
+| `/ocr` | POST | Legacy OCR endpoint |
+
+### Image Input Methods
+
+PaddleOCR-VL accepts images in multiple formats:
+
+```javascript
+// Base64 data URL
+"data:image/png;base64,iVBORw0KGgo..."
+
+// HTTP URL
+"https://example.com/document.png"
+
+// Raw base64
+"iVBORw0KGgo..."
+```
+
+**Supported formats:** PNG, JPEG, WebP, BMP, GIF, TIFF
+
+**Optimal resolution:** 1080p–2K. Images are automatically scaled for best results.
+
+### Performance
+
+| Mode | Speed per Page |
+|------|----------------|
+| GPU (CUDA) | 2–5 seconds |
+| CPU | 30–60 seconds |
+
+---
+
+## 🐳 Docker Compose

 ```yaml
 version: '3.8'
 services:
+  # General vision tasks
  minicpm:
    image: code.foss.global/host.today/ht-docker-ai:minicpm45v
-    container_name: minicpm
    ports:
      - "11434:11434"
    volumes:
@@ -124,11 +206,50 @@ services:
              capabilities: [gpu]
    restart: unless-stopped

+  # Document parsing / OCR
+  paddleocr:
+    image: code.foss.global/host.today/ht-docker-ai:paddleocr-vl
+    ports:
+      - "8000:8000"
+    volumes:
+      - hf-cache:/root/.cache/huggingface
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+    restart: unless-stopped
+
 volumes:
  ollama-data:
+  hf-cache:
 ```

-## Building Locally
+---
+
+## ⚙️ Environment Variables
+
+### MiniCPM-V 4.5
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `MODEL_NAME` | `minicpm-v` | Ollama model to pull on startup |
+| `OLLAMA_HOST` | `0.0.0.0` | API bind address |
+| `OLLAMA_ORIGINS` | `*` | Allowed CORS origins |
+
+### PaddleOCR-VL
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `MODEL_NAME` | `PaddlePaddle/PaddleOCR-VL` | HuggingFace model ID |
+| `SERVER_HOST` | `0.0.0.0` | API bind address |
+| `SERVER_PORT` | `8000` | API port |
+
+---
+
+## 🔧 Building from Source

 ```bash
 # Clone the repository
@@ -142,6 +263,77 @@ cd ht-docker-ai
 ./test-images.sh
 ```

-## License
+---

-MIT - Task Venture Capital GmbH
+## 🏗️ Architecture Notes
+
+### Dual-VLM Consensus Strategy
+
+For production document extraction, consider using both models together:
+
+1. **Pass 1:** MiniCPM-V visual extraction (images → JSON)
+2. **Pass 2:** PaddleOCR-VL table recognition (images → markdown → JSON)
+3. **Consensus:** If results match → Done (fast path)
+4. **Pass 3+:** Additional visual passes if needed
+
+This dual-VLM approach catches extraction errors that single models miss.
+
+### Why This Works
+
+- **Different architectures:** Two independent models cross-validate each other
+- **Specialized strengths:** PaddleOCR-VL excels at tables; MiniCPM-V handles general vision
+- **Native processing:** Both VLMs see original images—no intermediate HTML/structure loss
+
+---
+
+## 🔍 Troubleshooting
+
+### Model download hangs
+```bash
+docker logs -f <container-name>
+```
+Model downloads can take several minutes (~5GB for MiniCPM-V).
+
+### Out of memory
+- **GPU:** Use the CPU variant or upgrade VRAM
+- **CPU:** Increase container memory: `--memory=16g`
+
+### API not responding
+1. Check container health: `docker ps`
+2. Review logs: `docker logs <container>`
+3. Verify port: `curl localhost:11434/api/tags` or `curl localhost:8000/health`
+
+### Enable NVIDIA GPU support on host
+```bash
+# Install NVIDIA Container Toolkit
+curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
+curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
+  sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
+  sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
+sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
+sudo nvidia-ctk runtime configure --runtime=docker
+sudo systemctl restart docker
+```
+
+---
+
+## License and Legal Information
+
+This repository contains open-source code licensed under the MIT License. A copy of the license can be found in the [LICENSE](./LICENSE) file.
+
+**Please note:** The MIT License does not grant permission to use the trade names, trademarks, service marks, or product names of the project, except as required for reasonable and customary use in describing the origin of the work and reproducing the content of the NOTICE file.
+
+### Trademarks
+
+This project is owned and maintained by Task Venture Capital GmbH. The names and logos associated with Task Venture Capital GmbH and any related products or services are trademarks of Task Venture Capital GmbH or third parties, and are not included within the scope of the MIT license granted herein.
+
+Use of these trademarks must comply with Task Venture Capital GmbH's Trademark Guidelines or the guidelines of the respective third-party owners, and any usage must be approved in writing. Third-party trademarks used herein are the property of their respective owners and used only in a descriptive manner, e.g. for an implementation of an API or similar.
+
+### Company Information
+
+Task Venture Capital GmbH
+Registered at District Court Bremen HRB 35230 HB, Germany
+
+For any legal inquiries or further information, please contact us via email at hello@task.vc.
+
+By using this repository, you acknowledge that you have read this section, agree to comply with its terms, and understand that the licensing of the code does not imply endorsement by Task Venture Capital GmbH of any derivative works.
--- a/test/helpers/docker.ts
+++ b/test/helpers/docker.ts
@@ -0,0 +1,360 @@
+import { execSync } from 'child_process';
+
+// Project container names (only manage these)
+const PROJECT_CONTAINERS = [
+  'paddleocr-vl-test',
+  'paddleocr-vl-gpu-test',
+  'paddleocr-vl-cpu-test',
+  'paddleocr-vl-full-test',
+  'minicpm-test',
+];
+
+// Image configurations
+export interface IImageConfig {
+  name: string;
+  dockerfile: string;
+  buildContext: string;
+  containerName: string;
+  ports: string[];
+  volumes?: string[];
+  gpus?: boolean;
+  healthEndpoint?: string;
+  healthTimeout?: number;
+}
+
+export const IMAGES = {
+  paddleocrVlGpu: {
+    name: 'paddleocr-vl-gpu',
+    dockerfile: 'Dockerfile_paddleocr_vl_gpu',
+    buildContext: '.',
+    containerName: 'paddleocr-vl-test',
+    ports: ['8000:8000'],
+    volumes: ['ht-huggingface-cache:/root/.cache/huggingface'],
+    gpus: true,
+    healthEndpoint: 'http://localhost:8000/health',
+    healthTimeout: 300000, // 5 minutes for model loading
+  } as IImageConfig,
+
+  paddleocrVlCpu: {
+    name: 'paddleocr-vl-cpu',
+    dockerfile: 'Dockerfile_paddleocr_vl_cpu',
+    buildContext: '.',
+    containerName: 'paddleocr-vl-test',
+    ports: ['8000:8000'],
+    volumes: ['ht-huggingface-cache:/root/.cache/huggingface'],
+    gpus: false,
+    healthEndpoint: 'http://localhost:8000/health',
+    healthTimeout: 300000,
+  } as IImageConfig,
+
+  minicpm: {
+    name: 'minicpm45v',
+    dockerfile: 'Dockerfile_minicpm45v_gpu',
+    buildContext: '.',
+    containerName: 'minicpm-test',
+    ports: ['11434:11434'],
+    volumes: ['ht-ollama-models:/root/.ollama'],
+    gpus: true,
+    healthEndpoint: 'http://localhost:11434/api/tags',
+    healthTimeout: 120000,
+  } as IImageConfig,
+
+  // Full PaddleOCR-VL pipeline with PP-DocLayoutV2 + structured JSON output
+  paddleocrVlFull: {
+    name: 'paddleocr-vl-full',
+    dockerfile: 'Dockerfile_paddleocr_vl_full',
+    buildContext: '.',
+    containerName: 'paddleocr-vl-full-test',
+    ports: ['8000:8000'],
+    volumes: [
+      'ht-huggingface-cache:/root/.cache/huggingface',
+      'ht-paddleocr-cache:/root/.paddleocr',
+    ],
+    gpus: true,
+    healthEndpoint: 'http://localhost:8000/health',
+    healthTimeout: 600000, // 10 minutes for model loading (vLLM + PP-DocLayoutV2)
+  } as IImageConfig,
+};
+
+/**
+ * Execute a shell command and return output
+ */
+function exec(command: string, silent = false): string {
+  try {
+    return execSync(command, {
+      encoding: 'utf-8',
+      stdio: silent ? 'pipe' : 'inherit',
+    });
+  } catch (err: unknown) {
+    if (silent) return '';
+    throw err;
+  }
+}
+
+/**
+ * Check if a Docker image exists locally
+ */
+export function imageExists(imageName: string): boolean {
+  const result = exec(`docker images -q ${imageName}`, true);
+  return result.trim().length > 0;
+}
+
+/**
+ * Check if a container is running
+ */
+export function isContainerRunning(containerName: string): boolean {
+  const result = exec(`docker ps --filter "name=^${containerName}$" --format "{{.Names}}"`, true);
+  return result.trim() === containerName;
+}
+
+/**
+ * Check if a container exists (running or stopped)
+ */
+export function containerExists(containerName: string): boolean {
+  const result = exec(`docker ps -a --filter "name=^${containerName}$" --format "{{.Names}}"`, true);
+  return result.trim() === containerName;
+}
+
+/**
+ * Stop and remove a container
+ */
+export function removeContainer(containerName: string): void {
+  if (containerExists(containerName)) {
+    console.log(`[Docker] Removing container: ${containerName}`);
+    exec(`docker rm -f ${containerName}`, true);
+  }
+}
+
+/**
+ * Stop all project containers that conflict with the required one
+ */
+export function stopConflictingContainers(requiredContainer: string, requiredPort: string): void {
+  // Stop project containers using the same port
+  for (const container of PROJECT_CONTAINERS) {
+    if (container === requiredContainer) continue;
+
+    if (isContainerRunning(container)) {
+      // Check if this container uses the same port
+      const ports = exec(`docker port ${container} 2>/dev/null || true`, true);
+      if (ports.includes(requiredPort.split(':')[0])) {
+        console.log(`[Docker] Stopping conflicting container: ${container}`);
+        exec(`docker stop ${container}`, true);
+      }
+    }
+  }
+}
+
+/**
+ * Build a Docker image
+ */
+export function buildImage(config: IImageConfig): void {
+  console.log(`[Docker] Building image: ${config.name}`);
+  const cmd = `docker build --load -f ${config.dockerfile} -t ${config.name} ${config.buildContext}`;
+  exec(cmd);
+}
+
+/**
+ * Start a container from an image
+ */
+export function startContainer(config: IImageConfig): void {
+  // Remove existing container if it exists
+  removeContainer(config.containerName);
+
+  console.log(`[Docker] Starting container: ${config.containerName}`);
+
+  const portArgs = config.ports.map((p) => `-p ${p}`).join(' ');
+  const volumeArgs = config.volumes?.map((v) => `-v ${v}`).join(' ') || '';
+  const gpuArgs = config.gpus ? '--gpus all' : '';
+
+  const cmd = `docker run -d --name ${config.containerName} ${gpuArgs} ${portArgs} ${volumeArgs} ${config.name}`;
+  exec(cmd);
+}
+
+/**
+ * Wait for a container to become healthy
+ */
+export async function waitForHealth(
+  endpoint: string,
+  timeoutMs: number = 120000,
+  intervalMs: number = 5000
+): Promise<boolean> {
+  const startTime = Date.now();
+  console.log(`[Docker] Waiting for health: ${endpoint}`);
+
+  while (Date.now() - startTime < timeoutMs) {
+    try {
+      const response = await fetch(endpoint, {
+        method: 'GET',
+        signal: AbortSignal.timeout(5000),
+      });
+      if (response.ok) {
+        console.log(`[Docker] Service healthy!`);
+        return true;
+      }
+    } catch {
+      // Service not ready yet
+    }
+
+    const elapsed = Math.round((Date.now() - startTime) / 1000);
+    console.log(`[Docker] Waiting... (${elapsed}s)`);
+    await new Promise((resolve) => setTimeout(resolve, intervalMs));
+  }
+
+  console.log(`[Docker] Health check timeout after ${timeoutMs / 1000}s`);
+  return false;
+}
+
+/**
+ * Ensure a service is running and healthy
+ * - Builds image if missing
+ * - Stops conflicting project containers
+ * - Starts container if not running
+ * - Waits for health check
+ */
+export async function ensureService(config: IImageConfig): Promise<boolean> {
+  console.log(`\n[Docker] Ensuring service: ${config.name}`);
+
+  // Build image if it doesn't exist
+  if (!imageExists(config.name)) {
+    console.log(`[Docker] Image not found, building...`);
+    buildImage(config);
+  }
+
+  // Stop conflicting containers on the same port
+  const mainPort = config.ports[0];
+  stopConflictingContainers(config.containerName, mainPort);
+
+  // Start container if not running
+  if (!isContainerRunning(config.containerName)) {
+    startContainer(config);
+  } else {
+    console.log(`[Docker] Container already running: ${config.containerName}`);
+  }
+
+  // Wait for health
+  if (config.healthEndpoint) {
+    return waitForHealth(config.healthEndpoint, config.healthTimeout);
+  }
+
+  return true;
+}
+
+/**
+ * Ensure PaddleOCR-VL GPU service is running
+ */
+export async function ensurePaddleOcrVlGpu(): Promise<boolean> {
+  return ensureService(IMAGES.paddleocrVlGpu);
+}
+
+/**
+ * Ensure PaddleOCR-VL CPU service is running
+ */
+export async function ensurePaddleOcrVlCpu(): Promise<boolean> {
+  return ensureService(IMAGES.paddleocrVlCpu);
+}
+
+/**
+ * Ensure MiniCPM service is running
+ */
+export async function ensureMiniCpm(): Promise<boolean> {
+  return ensureService(IMAGES.minicpm);
+}
+
+/**
+ * Check if GPU is available
+ */
+export function isGpuAvailable(): boolean {
+  try {
+    const result = exec('nvidia-smi --query-gpu=name --format=csv,noheader 2>/dev/null', true);
+    return result.trim().length > 0;
+  } catch {
+    return false;
+  }
+}
+
+/**
+ * Ensure PaddleOCR-VL service (auto-detect GPU/CPU)
+ */
+export async function ensurePaddleOcrVl(): Promise<boolean> {
+  if (isGpuAvailable()) {
+    console.log('[Docker] GPU detected, using GPU image');
+    return ensurePaddleOcrVlGpu();
+  } else {
+    console.log('[Docker] No GPU detected, using CPU image');
+    return ensurePaddleOcrVlCpu();
+  }
+}
+
+/**
+ * Ensure PaddleOCR-VL Full Pipeline service (PP-DocLayoutV2 + structured output)
+ * This is the recommended service for production use - outputs structured JSON/Markdown
+ */
+export async function ensurePaddleOcrVlFull(): Promise<boolean> {
+  if (!isGpuAvailable()) {
+    console.log('[Docker] WARNING: Full pipeline requires GPU, but none detected');
+  }
+  return ensureService(IMAGES.paddleocrVlFull);
+}
+
+/**
+ * Ensure an Ollama model is pulled and available
+ * Uses the MiniCPM container (which runs Ollama) to pull the model
+ */
+export async function ensureOllamaModel(modelName: string): Promise<boolean> {
+  const OLLAMA_URL = 'http://localhost:11434';
+
+  console.log(`\n[Ollama] Ensuring model: ${modelName}`);
+
+  // Check if model exists
+  try {
+    const response = await fetch(`${OLLAMA_URL}/api/tags`);
+    if (response.ok) {
+      const data = await response.json();
+      const models = data.models || [];
+      const exists = models.some((m: { name: string }) =>
+        m.name === modelName || m.name.startsWith(modelName.split(':')[0])
+      );
+
+      if (exists) {
+        console.log(`[Ollama] Model already available: ${modelName}`);
+        return true;
+      }
+    }
+  } catch {
+    console.log(`[Ollama] Cannot check models, Ollama may not be running`);
+    return false;
+  }
+
+  // Pull the model
+  console.log(`[Ollama] Pulling model: ${modelName} (this may take a while)...`);
+  try {
+    const response = await fetch(`${OLLAMA_URL}/api/pull`, {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify({ name: modelName, stream: false }),
+    });
+
+    if (response.ok) {
+      console.log(`[Ollama] Model pulled successfully: ${modelName}`);
+      return true;
+    } else {
+      console.log(`[Ollama] Failed to pull model: ${response.status}`);
+      return false;
+    }
+  } catch (err) {
+    console.log(`[Ollama] Error pulling model: ${err}`);
+    return false;
+  }
+}
+
+/**
+ * Ensure Qwen2.5 7B model is available (for text-only JSON extraction)
+ */
+export async function ensureQwen25(): Promise<boolean> {
+  // First ensure the Ollama service (MiniCPM container) is running
+  const ollamaOk = await ensureMiniCpm();
+  if (!ollamaOk) return false;
+
+  // Then ensure the Qwen2.5 model is pulled
+  return ensureOllamaModel('qwen2.5:7b');
+}
--- a/test/test.bankstatements.combined.ts
+++ b/test/test.bankstatements.combined.ts
@@ -1,15 +1,23 @@
+/**
+ * Bank statement extraction test using MiniCPM-V (visual) + PaddleOCR-VL (table recognition)
+ *
+ * This is the combined/dual-VLM approach that uses both models for consensus:
+ *   - MiniCPM-V for visual extraction
+ *   - PaddleOCR-VL for table recognition
+ */
 import { tap, expect } from '@git.zone/tstest/tapbundle';
 import * as fs from 'fs';
 import * as path from 'path';
 import { execSync } from 'child_process';
 import * as os from 'os';
+import { ensurePaddleOcrVl, ensureMiniCpm } from './helpers/docker.js';

 // Service URLs
 const OLLAMA_URL = 'http://localhost:11434';
 const PADDLEOCR_VL_URL = 'http://localhost:8000';

 // Models
-const MINICPM_MODEL = 'openbmb/minicpm-v4.5:q8_0';
+const MINICPM_MODEL = 'minicpm-v:latest';
 const PADDLEOCR_VL_MODEL = 'paddleocr-vl';

 // Prompt for MiniCPM-V visual extraction
@@ -477,11 +485,18 @@ function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: strin

 // Tests

-tap.test('should connect to Ollama API', async () => {
-  const response = await fetch(`${OLLAMA_URL}/api/tags`);
-  expect(response.ok).toBeTrue();
-  const data = await response.json();
-  expect(data.models).toBeArray();
+tap.test('setup: ensure Docker containers are running', async () => {
+  console.log('\n[Setup] Checking Docker containers...\n');
+
+  // Ensure PaddleOCR-VL is running (auto-detects GPU/CPU)
+  const paddleOk = await ensurePaddleOcrVl();
+  expect(paddleOk).toBeTrue();
+
+  // Ensure MiniCPM is running
+  const minicpmOk = await ensureMiniCpm();
+  expect(minicpmOk).toBeTrue();
+
+  console.log('\n[Setup] All containers ready!\n');
 });

 tap.test('should have MiniCPM-V 4.5 model loaded', async () => {
@@ -494,8 +509,7 @@ tap.test('should have MiniCPM-V 4.5 model loaded', async () => {
 tap.test('should check PaddleOCR-VL availability', async () => {
  const available = await isPaddleOCRVLAvailable();
  console.log(`PaddleOCR-VL available: ${available}`);
-  // This test passes regardless - PaddleOCR-VL is optional
-  expect(true).toBeTrue();
+  expect(available).toBeTrue();
 });

 // Dynamic test for each PDF/JSON pair
--- a/test/test.bankstatements.minicpm.ts
+++ b/test/test.bankstatements.minicpm.ts
@@ -0,0 +1,334 @@
+/**
+ * Bank statement extraction test using MiniCPM-V only (visual extraction)
+ *
+ * This tests MiniCPM-V's ability to extract bank transactions directly from images
+ * without any OCR augmentation.
+ */
+import { tap, expect } from '@git.zone/tstest/tapbundle';
+import * as fs from 'fs';
+import * as path from 'path';
+import { execSync } from 'child_process';
+import * as os from 'os';
+import { ensureMiniCpm } from './helpers/docker.js';
+
+// Service URL
+const OLLAMA_URL = 'http://localhost:11434';
+
+// Model
+const MINICPM_MODEL = 'minicpm-v:latest';
+
+// Prompt for MiniCPM-V visual extraction
+const MINICPM_EXTRACT_PROMPT = `/nothink
+You are a bank statement parser. Extract EVERY transaction from the table.
+
+Read the Amount column carefully:
+- "- 21,47 €" means DEBIT, output as: -21.47
+- "+ 1.000,00 €" means CREDIT, output as: 1000.00
+- European format: comma = decimal point
+
+For each row output: {"date":"YYYY-MM-DD","counterparty":"NAME","amount":-21.47}
+
+Do not skip any rows. Return ONLY the JSON array, no explanation.`;
+
+interface ITransaction {
+  date: string;
+  counterparty: string;
+  amount: number;
+}
+
+/**
+ * Convert PDF to PNG images using ImageMagick
+ */
+function convertPdfToImages(pdfPath: string): string[] {
+  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
+  const outputPattern = path.join(tempDir, 'page-%d.png');
+
+  try {
+    execSync(
+      `convert -density 300 -quality 100 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
+      { stdio: 'pipe' }
+    );
+
+    const files = fs.readdirSync(tempDir).filter((f: string) => f.endsWith('.png')).sort();
+    const images: string[] = [];
+
+    for (const file of files) {
+      const imagePath = path.join(tempDir, file);
+      const imageData = fs.readFileSync(imagePath);
+      images.push(imageData.toString('base64'));
+    }
+
+    return images;
+  } finally {
+    fs.rmSync(tempDir, { recursive: true, force: true });
+  }
+}
+
+/**
+ * Extract using MiniCPM-V via Ollama
+ */
+async function extractWithMiniCPM(images: string[], passLabel: string): Promise<ITransaction[]> {
+  const payload = {
+    model: MINICPM_MODEL,
+    prompt: MINICPM_EXTRACT_PROMPT,
+    images,
+    stream: true,
+    options: {
+      num_predict: 16384,
+      temperature: 0.1,
+    },
+  };
+
+  const response = await fetch(`${OLLAMA_URL}/api/generate`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify(payload),
+  });
+
+  if (!response.ok) {
+    throw new Error(`Ollama API error: ${response.status}`);
+  }
+
+  const reader = response.body?.getReader();
+  if (!reader) {
+    throw new Error('No response body');
+  }
+
+  const decoder = new TextDecoder();
+  let fullText = '';
+  let lineBuffer = '';
+
+  console.log(`[${passLabel}] Extracting with MiniCPM-V...`);
+
+  while (true) {
+    const { done, value } = await reader.read();
+    if (done) break;
+
+    const chunk = decoder.decode(value, { stream: true });
+    const lines = chunk.split('\n').filter((l) => l.trim());
+
+    for (const line of lines) {
+      try {
+        const json = JSON.parse(line);
+        if (json.response) {
+          fullText += json.response;
+          lineBuffer += json.response;
+
+          if (lineBuffer.includes('\n')) {
+            const parts = lineBuffer.split('\n');
+            for (let i = 0; i < parts.length - 1; i++) {
+              console.log(parts[i]);
+            }
+            lineBuffer = parts[parts.length - 1];
+          }
+        }
+      } catch {
+        // Skip invalid JSON lines
+      }
+    }
+  }
+
+  if (lineBuffer) {
+    console.log(lineBuffer);
+  }
+  console.log('');
+
+  const startIdx = fullText.indexOf('[');
+  const endIdx = fullText.lastIndexOf(']') + 1;
+
+  if (startIdx < 0 || endIdx <= startIdx) {
+    throw new Error('No JSON array found in response');
+  }
+
+  return JSON.parse(fullText.substring(startIdx, endIdx));
+}
+
+/**
+ * Create a hash of transactions for comparison
+ */
+function hashTransactions(transactions: ITransaction[]): string {
+  return transactions
+    .map((t) => `${t.date}|${t.amount.toFixed(2)}`)
+    .sort()
+    .join(';');
+}
+
+/**
+ * Extract with consensus voting using MiniCPM-V only
+ */
+async function extractWithConsensus(
+  images: string[],
+  maxPasses: number = 5
+): Promise<ITransaction[]> {
+  const results: Array<{ transactions: ITransaction[]; hash: string }> = [];
+  const hashCounts: Map<string, number> = new Map();
+
+  const addResult = (transactions: ITransaction[], passLabel: string): number => {
+    const hash = hashTransactions(transactions);
+    results.push({ transactions, hash });
+    hashCounts.set(hash, (hashCounts.get(hash) || 0) + 1);
+    console.log(
+      `[${passLabel}] Got ${transactions.length} transactions (hash: ${hash.substring(0, 20)}...)`
+    );
+    return hashCounts.get(hash)!;
+  };
+
+  console.log('[Setup] Using MiniCPM-V only');
+
+  for (let pass = 1; pass <= maxPasses; pass++) {
+    try {
+      const transactions = await extractWithMiniCPM(images, `Pass ${pass} MiniCPM-V`);
+      const count = addResult(transactions, `Pass ${pass} MiniCPM-V`);
+
+      if (count >= 2) {
+        console.log(`[Consensus] Reached after ${pass} passes`);
+        return transactions;
+      }
+
+      console.log(`[Pass ${pass}] No consensus yet, trying again...`);
+    } catch (err) {
+      console.log(`[Pass ${pass}] Error: ${err}`);
+    }
+  }
+
+  // No consensus reached - return the most common result
+  let bestHash = '';
+  let bestCount = 0;
+  for (const [hash, count] of hashCounts) {
+    if (count > bestCount) {
+      bestCount = count;
+      bestHash = hash;
+    }
+  }
+
+  if (!bestHash) {
+    throw new Error('No valid results obtained');
+  }
+
+  const best = results.find((r) => r.hash === bestHash)!;
+  console.log(`[No consensus] Using most common result (${bestCount}/${maxPasses} passes)`);
+  return best.transactions;
+}
+
+/**
+ * Compare extracted transactions against expected
+ */
+function compareTransactions(
+  extracted: ITransaction[],
+  expected: ITransaction[]
+): { matches: number; total: number; errors: string[] } {
+  const errors: string[] = [];
+  let matches = 0;
+
+  for (let i = 0; i < expected.length; i++) {
+    const exp = expected[i];
+    const ext = extracted[i];
+
+    if (!ext) {
+      errors.push(`Missing transaction ${i}: ${exp.date} ${exp.counterparty}`);
+      continue;
+    }
+
+    const dateMatch = ext.date === exp.date;
+    const amountMatch = Math.abs(ext.amount - exp.amount) < 0.01;
+
+    if (dateMatch && amountMatch) {
+      matches++;
+    } else {
+      errors.push(
+        `Mismatch at ${i}: expected ${exp.date}/${exp.amount}, got ${ext.date}/${ext.amount}`
+      );
+    }
+  }
+
+  if (extracted.length > expected.length) {
+    errors.push(`Extra transactions: ${extracted.length - expected.length}`);
+  }
+
+  return { matches, total: expected.length, errors };
+}
+
+/**
+ * Find all test cases (PDF + JSON pairs) in .nogit/
+ */
+function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
+  const testDir = path.join(process.cwd(), '.nogit');
+  if (!fs.existsSync(testDir)) {
+    return [];
+  }
+
+  const files = fs.readdirSync(testDir);
+  const pdfFiles = files.filter((f: string) => f.endsWith('.pdf'));
+  const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
+
+  for (const pdf of pdfFiles) {
+    const baseName = pdf.replace('.pdf', '');
+    const jsonFile = `${baseName}.json`;
+    if (files.includes(jsonFile)) {
+      testCases.push({
+        name: baseName,
+        pdfPath: path.join(testDir, pdf),
+        jsonPath: path.join(testDir, jsonFile),
+      });
+    }
+  }
+
+  return testCases;
+}
+
+// Tests
+
+tap.test('setup: ensure Docker containers are running', async () => {
+  console.log('\n[Setup] Checking Docker containers...\n');
+
+  // Ensure MiniCPM is running
+  const minicpmOk = await ensureMiniCpm();
+  expect(minicpmOk).toBeTrue();
+
+  console.log('\n[Setup] All containers ready!\n');
+});
+
+tap.test('should have MiniCPM-V 4.5 model loaded', async () => {
+  const response = await fetch(`${OLLAMA_URL}/api/tags`);
+  const data = await response.json();
+  const modelNames = data.models.map((m: { name: string }) => m.name);
+  expect(modelNames.some((name: string) => name.includes('minicpm-v4.5'))).toBeTrue();
+});
+
+// Dynamic test for each PDF/JSON pair
+const testCases = findTestCases();
+console.log(`\nFound ${testCases.length} bank statement test cases (MiniCPM-V only)\n`);
+
+for (const testCase of testCases) {
+  tap.test(`should extract transactions from ${testCase.name}`, async () => {
+    // Load expected transactions
+    const expected: ITransaction[] = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
+    console.log(`\n=== ${testCase.name} ===`);
+    console.log(`Expected: ${expected.length} transactions`);
+
+    // Convert PDF to images
+    console.log('Converting PDF to images...');
+    const images = convertPdfToImages(testCase.pdfPath);
+    console.log(`Converted: ${images.length} pages\n`);
+
+    // Extract with consensus (MiniCPM-V only)
+    const extracted = await extractWithConsensus(images);
+    console.log(`\nFinal: ${extracted.length} transactions`);
+
+    // Compare results
+    const result = compareTransactions(extracted, expected);
+    console.log(`Accuracy: ${result.matches}/${result.total}`);
+
+    if (result.errors.length > 0) {
+      console.log('Errors:');
+      result.errors.forEach((e) => console.log(`  - ${e}`));
+    }
+
+    // Assert high accuracy
+    const accuracy = result.matches / result.total;
+    expect(accuracy).toBeGreaterThan(0.95);
+    expect(extracted.length).toEqual(expected.length);
+  });
+}
+
+export default tap.start();
--- a/test/test.bankstatements.paddleocr-vl.ts
+++ b/test/test.bankstatements.paddleocr-vl.ts
@@ -0,0 +1,346 @@
+/**
+ * Bank statement extraction test using PaddleOCR-VL Full Pipeline
+ *
+ * This tests the complete PaddleOCR-VL pipeline for bank statements:
+ *   1. PP-DocLayoutV2 for layout detection
+ *   2. PaddleOCR-VL for recognition (tables with proper structure)
+ *   3. Structured Markdown output with tables
+ *   4. MiniCPM extracts transactions from structured tables
+ *
+ * The structured Markdown has properly formatted tables,
+ * making it much easier for MiniCPM to extract transaction data.
+ */
+import { tap, expect } from '@git.zone/tstest/tapbundle';
+import * as fs from 'fs';
+import * as path from 'path';
+import { execSync } from 'child_process';
+import * as os from 'os';
+import { ensurePaddleOcrVlFull, ensureMiniCpm } from './helpers/docker.js';
+
+const PADDLEOCR_VL_URL = 'http://localhost:8000';
+const OLLAMA_URL = 'http://localhost:11434';
+const MINICPM_MODEL = 'minicpm-v:latest';
+
+interface ITransaction {
+  date: string;
+  counterparty: string;
+  amount: number;
+}
+
+/**
+ * Convert PDF to PNG images using ImageMagick
+ */
+function convertPdfToImages(pdfPath: string): string[] {
+  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
+  const outputPattern = path.join(tempDir, 'page-%d.png');
+
+  try {
+    execSync(
+      `convert -density 300 -quality 100 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
+      { stdio: 'pipe' }
+    );
+
+    const files = fs.readdirSync(tempDir).filter((f: string) => f.endsWith('.png')).sort();
+    const images: string[] = [];
+
+    for (const file of files) {
+      const imagePath = path.join(tempDir, file);
+      const imageData = fs.readFileSync(imagePath);
+      images.push(imageData.toString('base64'));
+    }
+
+    return images;
+  } finally {
+    fs.rmSync(tempDir, { recursive: true, force: true });
+  }
+}
+
+/**
+ * Parse document using PaddleOCR-VL Full Pipeline (returns structured Markdown)
+ */
+async function parseDocument(imageBase64: string): Promise<string> {
+  const response = await fetch(`${PADDLEOCR_VL_URL}/parse`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({
+      image: imageBase64,
+      output_format: 'markdown',
+    }),
+  });
+
+  if (!response.ok) {
+    const text = await response.text();
+    throw new Error(`PaddleOCR-VL API error: ${response.status} - ${text}`);
+  }
+
+  const data = await response.json();
+
+  if (!data.success) {
+    throw new Error(`PaddleOCR-VL error: ${data.error}`);
+  }
+
+  return data.result?.markdown || '';
+}
+
+/**
+ * Extract transactions from structured Markdown using MiniCPM
+ */
+async function extractTransactionsFromMarkdown(markdown: string): Promise<ITransaction[]> {
+  console.log(`    [Extract] Processing ${markdown.length} chars of Markdown`);
+
+  const prompt = `/nothink
+Convert this bank statement to a JSON array of transactions.
+
+Read the Amount values carefully:
+- "- 21,47 €" means DEBIT, output as: -21.47
+- "+ 1.000,00 €" means CREDIT, output as: 1000.00
+- European format: comma = decimal point, dot = thousands
+
+For each transaction output: {"date":"YYYY-MM-DD","counterparty":"NAME","amount":-21.47}
+
+Return ONLY the JSON array, no explanation.
+
+Document:
+${markdown}`;
+
+  const payload = {
+    model: MINICPM_MODEL,
+    prompt,
+    stream: true,
+    options: {
+      num_predict: 16384,
+      temperature: 0.1,
+    },
+  };
+
+  const response = await fetch(`${OLLAMA_URL}/api/generate`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify(payload),
+  });
+
+  if (!response.ok) {
+    throw new Error(`Ollama API error: ${response.status}`);
+  }
+
+  const reader = response.body?.getReader();
+  if (!reader) {
+    throw new Error('No response body');
+  }
+
+  const decoder = new TextDecoder();
+  let fullText = '';
+
+  while (true) {
+    const { done, value } = await reader.read();
+    if (done) break;
+
+    const chunk = decoder.decode(value, { stream: true });
+    const lines = chunk.split('\n').filter((l) => l.trim());
+
+    for (const line of lines) {
+      try {
+        const json = JSON.parse(line);
+        if (json.response) {
+          fullText += json.response;
+        }
+      } catch {
+        // Skip invalid JSON lines
+      }
+    }
+  }
+
+  // Extract JSON array from response
+  const startIdx = fullText.indexOf('[');
+  const endIdx = fullText.lastIndexOf(']') + 1;
+
+  if (startIdx < 0 || endIdx <= startIdx) {
+    throw new Error(`No JSON array found in response: ${fullText.substring(0, 200)}`);
+  }
+
+  const jsonStr = fullText.substring(startIdx, endIdx);
+  return JSON.parse(jsonStr);
+}
+
+/**
+ * Extract transactions from all pages of a bank statement
+ */
+async function extractAllTransactions(images: string[]): Promise<ITransaction[]> {
+  const allTransactions: ITransaction[] = [];
+
+  for (let i = 0; i < images.length; i++) {
+    console.log(`  Processing page ${i + 1}/${images.length}...`);
+
+    // Parse with full pipeline
+    const markdown = await parseDocument(images[i]);
+    console.log(`    [Parse] Got ${markdown.split('\n').length} lines of Markdown`);
+
+    // Extract transactions
+    try {
+      const transactions = await extractTransactionsFromMarkdown(markdown);
+      console.log(`    [Extracted] ${transactions.length} transactions`);
+      allTransactions.push(...transactions);
+    } catch (err) {
+      console.log(`    [Error] ${err}`);
+    }
+  }
+
+  return allTransactions;
+}
+
+/**
+ * Compare transactions - find matching transaction in expected list
+ */
+function findMatchingTransaction(
+  tx: ITransaction,
+  expectedList: ITransaction[]
+): ITransaction | undefined {
+  return expectedList.find((exp) => {
+    const dateMatch = tx.date === exp.date;
+    const amountMatch = Math.abs(tx.amount - exp.amount) < 0.02;
+    const counterpartyMatch =
+      tx.counterparty?.toLowerCase().includes(exp.counterparty?.toLowerCase().slice(0, 10)) ||
+      exp.counterparty?.toLowerCase().includes(tx.counterparty?.toLowerCase().slice(0, 10));
+    return dateMatch && amountMatch && counterpartyMatch;
+  });
+}
+
+/**
+ * Calculate extraction accuracy
+ */
+function calculateAccuracy(
+  extracted: ITransaction[],
+  expected: ITransaction[]
+): { matched: number; total: number; accuracy: number } {
+  let matched = 0;
+  const usedExpected = new Set<number>();
+
+  for (const tx of extracted) {
+    for (let i = 0; i < expected.length; i++) {
+      if (usedExpected.has(i)) continue;
+
+      const exp = expected[i];
+      const dateMatch = tx.date === exp.date;
+      const amountMatch = Math.abs(tx.amount - exp.amount) < 0.02;
+
+      if (dateMatch && amountMatch) {
+        matched++;
+        usedExpected.add(i);
+        break;
+      }
+    }
+  }
+
+  return {
+    matched,
+    total: expected.length,
+    accuracy: expected.length > 0 ? (matched / expected.length) * 100 : 0,
+  };
+}
+
+/**
+ * Find all test cases (PDF + JSON pairs) in .nogit/bankstatements/
+ */
+function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
+  const testDir = path.join(process.cwd(), '.nogit/bankstatements');
+  if (!fs.existsSync(testDir)) {
+    return [];
+  }
+
+  const files = fs.readdirSync(testDir);
+  const pdfFiles = files.filter((f) => f.endsWith('.pdf'));
+  const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
+
+  for (const pdf of pdfFiles) {
+    const baseName = pdf.replace('.pdf', '');
+    const jsonFile = `${baseName}.json`;
+    if (files.includes(jsonFile)) {
+      testCases.push({
+        name: baseName,
+        pdfPath: path.join(testDir, pdf),
+        jsonPath: path.join(testDir, jsonFile),
+      });
+    }
+  }
+
+  testCases.sort((a, b) => a.name.localeCompare(b.name));
+  return testCases;
+}
+
+// Tests
+
+tap.test('setup: ensure Docker containers are running', async () => {
+  console.log('\n[Setup] Checking Docker containers...\n');
+
+  // Ensure PaddleOCR-VL Full Pipeline is running
+  const paddleOk = await ensurePaddleOcrVlFull();
+  expect(paddleOk).toBeTrue();
+
+  // Ensure MiniCPM is running (for field extraction from Markdown)
+  const minicpmOk = await ensureMiniCpm();
+  expect(minicpmOk).toBeTrue();
+
+  console.log('\n[Setup] All containers ready!\n');
+});
+
+// Dynamic test for each PDF/JSON pair
+const testCases = findTestCases();
+console.log(`\nFound ${testCases.length} bank statement test cases (PaddleOCR-VL Full Pipeline)\n`);
+
+const results: Array<{ name: string; accuracy: number; matched: number; total: number }> = [];
+
+for (const testCase of testCases) {
+  tap.test(`should extract bank statement: ${testCase.name}`, async () => {
+    // Load expected data
+    const expected: ITransaction[] = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
+    console.log(`\n=== ${testCase.name} ===`);
+    console.log(`Expected: ${expected.length} transactions`);
+
+    const startTime = Date.now();
+
+    // Convert PDF to images
+    const images = convertPdfToImages(testCase.pdfPath);
+    console.log(`  Pages: ${images.length}`);
+
+    // Extract all transactions
+    const extracted = await extractAllTransactions(images);
+
+    const endTime = Date.now();
+    const elapsedMs = endTime - startTime;
+
+    // Calculate accuracy
+    const accuracy = calculateAccuracy(extracted, expected);
+    results.push({
+      name: testCase.name,
+      accuracy: accuracy.accuracy,
+      matched: accuracy.matched,
+      total: accuracy.total,
+    });
+
+    console.log(`  Extracted: ${extracted.length} transactions`);
+    console.log(`  Matched: ${accuracy.matched}/${accuracy.total} (${accuracy.accuracy.toFixed(1)}%)`);
+    console.log(`  Time: ${(elapsedMs / 1000).toFixed(1)}s`);
+
+    // We expect at least 50% accuracy
+    expect(accuracy.accuracy).toBeGreaterThan(50);
+  });
+}
+
+tap.test('summary', async () => {
+  const totalStatements = results.length;
+  const avgAccuracy =
+    results.length > 0 ? results.reduce((a, b) => a + b.accuracy, 0) / results.length : 0;
+  const totalMatched = results.reduce((a, b) => a + b.matched, 0);
+  const totalExpected = results.reduce((a, b) => a + b.total, 0);
+
+  console.log(`\n======================================================`);
+  console.log(`  Bank Statement Extraction Summary (PaddleOCR-VL Full)`);
+  console.log(`======================================================`);
+  console.log(`  Method:      PaddleOCR-VL Full Pipeline -> MiniCPM`);
+  console.log(`  Statements:  ${totalStatements}`);
+  console.log(`  Transactions: ${totalMatched}/${totalExpected} matched`);
+  console.log(`  Avg accuracy: ${avgAccuracy.toFixed(1)}%`);
+  console.log(`======================================================\n`);
+});
+
+export default tap.start();
--- a/test/test.invoices.combined.ts
+++ b/test/test.invoices.combined.ts
@@ -1,11 +1,19 @@
+/**
+ * Invoice extraction test using MiniCPM-V (visual) + PaddleOCR-VL (OCR augmentation)
+ *
+ * This is the combined approach that uses both models for best accuracy:
+ *   - MiniCPM-V for visual understanding
+ *   - PaddleOCR-VL for OCR text to augment prompts
+ */
 import { tap, expect } from '@git.zone/tstest/tapbundle';
 import * as fs from 'fs';
 import * as path from 'path';
 import { execSync } from 'child_process';
 import * as os from 'os';
+import { ensurePaddleOcrVl, ensureMiniCpm } from './helpers/docker.js';

 const OLLAMA_URL = 'http://localhost:11434';
-const MODEL = 'openbmb/minicpm-v4.5:q8_0';
+const MODEL = 'minicpm-v:latest';
 const PADDLEOCR_VL_URL = 'http://localhost:8000';

 interface IInvoice {
@@ -358,11 +366,18 @@ function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: strin

 // Tests

-tap.test('should connect to Ollama API', async () => {
-  const response = await fetch(`${OLLAMA_URL}/api/tags`);
-  expect(response.ok).toBeTrue();
-  const data = await response.json();
-  expect(data.models).toBeArray();
+tap.test('setup: ensure Docker containers are running', async () => {
+  console.log('\n[Setup] Checking Docker containers...\n');
+
+  // Ensure PaddleOCR-VL is running (auto-detects GPU/CPU)
+  const paddleOk = await ensurePaddleOcrVl();
+  expect(paddleOk).toBeTrue();
+
+  // Ensure MiniCPM is running
+  const minicpmOk = await ensureMiniCpm();
+  expect(minicpmOk).toBeTrue();
+
+  console.log('\n[Setup] All containers ready!\n');
 });

 tap.test('should have MiniCPM-V 4.5 model loaded', async () => {
--- a/test/test.invoices.minicpm.ts
+++ b/test/test.invoices.minicpm.ts
@@ -0,0 +1,345 @@
+/**
+ * Invoice extraction test using MiniCPM-V only (visual extraction)
+ *
+ * This tests MiniCPM-V's ability to extract invoice data directly from images
+ * without any OCR augmentation.
+ */
+import { tap, expect } from '@git.zone/tstest/tapbundle';
+import * as fs from 'fs';
+import * as path from 'path';
+import { execSync } from 'child_process';
+import * as os from 'os';
+import { ensureMiniCpm } from './helpers/docker.js';
+
+const OLLAMA_URL = 'http://localhost:11434';
+const MODEL = 'minicpm-v:latest';
+
+interface IInvoice {
+  invoice_number: string;
+  invoice_date: string;
+  vendor_name: string;
+  currency: string;
+  net_amount: number;
+  vat_amount: number;
+  total_amount: number;
+}
+
+/**
+ * Build extraction prompt (MiniCPM-V only, no OCR augmentation)
+ */
+function buildPrompt(): string {
+  return `/nothink
+You are an invoice parser. Extract the following fields from this invoice:
+
+1. invoice_number: The invoice/receipt number
+2. invoice_date: Date in YYYY-MM-DD format
+3. vendor_name: Company that issued the invoice
+4. currency: EUR, USD, etc.
+5. net_amount: Amount before tax (if shown)
+6. vat_amount: Tax/VAT amount (if shown, 0 if reverse charge or no tax)
+7. total_amount: Final amount due
+
+Return ONLY valid JSON in this exact format:
+{"invoice_number":"XXX","invoice_date":"YYYY-MM-DD","vendor_name":"Company Name","currency":"EUR","net_amount":100.00,"vat_amount":19.00,"total_amount":119.00}
+
+If a field is not visible, use null for strings or 0 for numbers.
+No explanation, just the JSON object.`;
+}
+
+/**
+ * Convert PDF to PNG images using ImageMagick
+ */
+function convertPdfToImages(pdfPath: string): string[] {
+  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
+  const outputPattern = path.join(tempDir, 'page-%d.png');
+
+  try {
+    execSync(
+      `convert -density 200 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
+      { stdio: 'pipe' }
+    );
+
+    const files = fs.readdirSync(tempDir).filter((f) => f.endsWith('.png')).sort();
+    const images: string[] = [];
+
+    for (const file of files) {
+      const imagePath = path.join(tempDir, file);
+      const imageData = fs.readFileSync(imagePath);
+      images.push(imageData.toString('base64'));
+    }
+
+    return images;
+  } finally {
+    fs.rmSync(tempDir, { recursive: true, force: true });
+  }
+}
+
+/**
+ * Single extraction pass with MiniCPM-V
+ */
+async function extractOnce(images: string[], passNum: number): Promise<IInvoice> {
+  const payload = {
+    model: MODEL,
+    prompt: buildPrompt(),
+    images,
+    stream: true,
+    options: {
+      num_predict: 2048,
+      temperature: 0.1,
+    },
+  };
+
+  const response = await fetch(`${OLLAMA_URL}/api/generate`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify(payload),
+  });
+
+  if (!response.ok) {
+    throw new Error(`Ollama API error: ${response.status}`);
+  }
+
+  const reader = response.body?.getReader();
+  if (!reader) {
+    throw new Error('No response body');
+  }
+
+  const decoder = new TextDecoder();
+  let fullText = '';
+
+  while (true) {
+    const { done, value } = await reader.read();
+    if (done) break;
+
+    const chunk = decoder.decode(value, { stream: true });
+    const lines = chunk.split('\n').filter((l) => l.trim());
+
+    for (const line of lines) {
+      try {
+        const json = JSON.parse(line);
+        if (json.response) {
+          fullText += json.response;
+        }
+      } catch {
+        // Skip invalid JSON lines
+      }
+    }
+  }
+
+  // Extract JSON from response
+  const startIdx = fullText.indexOf('{');
+  const endIdx = fullText.lastIndexOf('}') + 1;
+
+  if (startIdx < 0 || endIdx <= startIdx) {
+    throw new Error(`No JSON object found in response: ${fullText.substring(0, 200)}`);
+  }
+
+  const jsonStr = fullText.substring(startIdx, endIdx);
+  return JSON.parse(jsonStr);
+}
+
+/**
+ * Create a hash of invoice for comparison (using key fields)
+ */
+function hashInvoice(invoice: IInvoice): string {
+  return `${invoice.invoice_number}|${invoice.invoice_date}|${invoice.total_amount.toFixed(2)}`;
+}
+
+/**
+ * Extract with consensus voting using MiniCPM-V only
+ */
+async function extractWithConsensus(images: string[], invoiceName: string, maxPasses: number = 5): Promise<IInvoice> {
+  const results: Array<{ invoice: IInvoice; hash: string }> = [];
+  const hashCounts: Map<string, number> = new Map();
+
+  const addResult = (invoice: IInvoice, passLabel: string): number => {
+    const hash = hashInvoice(invoice);
+    results.push({ invoice, hash });
+    hashCounts.set(hash, (hashCounts.get(hash) || 0) + 1);
+    console.log(`  [${passLabel}] ${invoice.invoice_number} | ${invoice.invoice_date} | ${invoice.total_amount} ${invoice.currency}`);
+    return hashCounts.get(hash)!;
+  };
+
+  for (let pass = 1; pass <= maxPasses; pass++) {
+    try {
+      const invoice = await extractOnce(images, pass);
+      const count = addResult(invoice, `Pass ${pass}`);
+
+      if (count >= 2) {
+        console.log(`  [Consensus] Reached after ${pass} passes`);
+        return invoice;
+      }
+    } catch (err) {
+      console.log(`  [Pass ${pass}] Error: ${err}`);
+    }
+  }
+
+  // No consensus reached - return the most common result
+  let bestHash = '';
+  let bestCount = 0;
+  for (const [hash, count] of hashCounts) {
+    if (count > bestCount) {
+      bestCount = count;
+      bestHash = hash;
+    }
+  }
+
+  if (!bestHash) {
+    throw new Error(`No valid results for ${invoiceName}`);
+  }
+
+  const best = results.find((r) => r.hash === bestHash)!;
+  console.log(`  [No consensus] Using most common result (${bestCount}/${maxPasses} passes)`);
+  return best.invoice;
+}
+
+/**
+ * Compare extracted invoice against expected
+ */
+function compareInvoice(
+  extracted: IInvoice,
+  expected: IInvoice
+): { match: boolean; errors: string[] } {
+  const errors: string[] = [];
+
+  // Compare invoice number (normalize by removing spaces and case)
+  const extNum = extracted.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
+  const expNum = expected.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
+  if (extNum !== expNum) {
+    errors.push(`invoice_number: expected "${expected.invoice_number}", got "${extracted.invoice_number}"`);
+  }
+
+  // Compare date
+  if (extracted.invoice_date !== expected.invoice_date) {
+    errors.push(`invoice_date: expected "${expected.invoice_date}", got "${extracted.invoice_date}"`);
+  }
+
+  // Compare total amount (with tolerance)
+  if (Math.abs(extracted.total_amount - expected.total_amount) > 0.02) {
+    errors.push(`total_amount: expected ${expected.total_amount}, got ${extracted.total_amount}`);
+  }
+
+  // Compare currency
+  if (extracted.currency?.toUpperCase() !== expected.currency?.toUpperCase()) {
+    errors.push(`currency: expected "${expected.currency}", got "${extracted.currency}"`);
+  }
+
+  return { match: errors.length === 0, errors };
+}
+
+/**
+ * Find all test cases (PDF + JSON pairs) in .nogit/invoices/
+ */
+function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
+  const testDir = path.join(process.cwd(), '.nogit/invoices');
+  if (!fs.existsSync(testDir)) {
+    return [];
+  }
+
+  const files = fs.readdirSync(testDir);
+  const pdfFiles = files.filter((f) => f.endsWith('.pdf'));
+  const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
+
+  for (const pdf of pdfFiles) {
+    const baseName = pdf.replace('.pdf', '');
+    const jsonFile = `${baseName}.json`;
+    if (files.includes(jsonFile)) {
+      testCases.push({
+        name: baseName,
+        pdfPath: path.join(testDir, pdf),
+        jsonPath: path.join(testDir, jsonFile),
+      });
+    }
+  }
+
+  // Sort alphabetically
+  testCases.sort((a, b) => a.name.localeCompare(b.name));
+
+  return testCases;
+}
+
+// Tests
+
+tap.test('setup: ensure Docker containers are running', async () => {
+  console.log('\n[Setup] Checking Docker containers...\n');
+
+  // Ensure MiniCPM is running
+  const minicpmOk = await ensureMiniCpm();
+  expect(minicpmOk).toBeTrue();
+
+  console.log('\n[Setup] All containers ready!\n');
+});
+
+tap.test('should have MiniCPM-V 4.5 model loaded', async () => {
+  const response = await fetch(`${OLLAMA_URL}/api/tags`);
+  const data = await response.json();
+  const modelNames = data.models.map((m: { name: string }) => m.name);
+  expect(modelNames.some((name: string) => name.includes('minicpm-v4.5'))).toBeTrue();
+});
+
+// Dynamic test for each PDF/JSON pair
+const testCases = findTestCases();
+console.log(`\nFound ${testCases.length} invoice test cases (MiniCPM-V only)\n`);
+
+let passedCount = 0;
+let failedCount = 0;
+const processingTimes: number[] = [];
+
+for (const testCase of testCases) {
+  tap.test(`should extract invoice: ${testCase.name}`, async () => {
+    // Load expected data
+    const expected: IInvoice = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
+    console.log(`\n=== ${testCase.name} ===`);
+    console.log(`Expected: ${expected.invoice_number} | ${expected.invoice_date} | ${expected.total_amount} ${expected.currency}`);
+
+    const startTime = Date.now();
+
+    // Convert PDF to images
+    const images = convertPdfToImages(testCase.pdfPath);
+    console.log(`  Pages: ${images.length}`);
+
+    // Extract with consensus voting (MiniCPM-V only)
+    const extracted = await extractWithConsensus(images, testCase.name);
+
+    const endTime = Date.now();
+    const elapsedMs = endTime - startTime;
+    processingTimes.push(elapsedMs);
+
+    // Compare results
+    const result = compareInvoice(extracted, expected);
+
+    if (result.match) {
+      passedCount++;
+      console.log(`  Result: MATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
+    } else {
+      failedCount++;
+      console.log(`  Result: MISMATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
+      result.errors.forEach((e) => console.log(`    - ${e}`));
+    }
+
+    // Assert match
+    expect(result.match).toBeTrue();
+  });
+}
+
+tap.test('summary', async () => {
+  const totalInvoices = testCases.length;
+  const accuracy = totalInvoices > 0 ? (passedCount / totalInvoices) * 100 : 0;
+  const totalTimeMs = processingTimes.reduce((a, b) => a + b, 0);
+  const avgTimeMs = processingTimes.length > 0 ? totalTimeMs / processingTimes.length : 0;
+  const avgTimeSec = avgTimeMs / 1000;
+  const totalTimeSec = totalTimeMs / 1000;
+
+  console.log(`\n========================================`);
+  console.log(`   Invoice Extraction Summary (MiniCPM)`);
+  console.log(`========================================`);
+  console.log(`  Passed:    ${passedCount}/${totalInvoices}`);
+  console.log(`  Failed:    ${failedCount}/${totalInvoices}`);
+  console.log(`  Accuracy:  ${accuracy.toFixed(1)}%`);
+  console.log(`----------------------------------------`);
+  console.log(`  Total time:   ${totalTimeSec.toFixed(1)}s`);
+  console.log(`  Avg per inv:  ${avgTimeSec.toFixed(1)}s`);
+  console.log(`========================================\n`);
+});
+
+export default tap.start();
--- a/test/test.invoices.paddleocr-vl.ts
+++ b/test/test.invoices.paddleocr-vl.ts
@@ -0,0 +1,461 @@
+/**
+ * Invoice extraction test using PaddleOCR-VL Full Pipeline
+ *
+ * This tests the complete PaddleOCR-VL pipeline:
+ *   1. PP-DocLayoutV2 for layout detection
+ *   2. PaddleOCR-VL for recognition
+ *   3. Structured HTML output (semantic tags with proper tables)
+ *   4. Qwen2.5 extracts invoice fields from structured HTML
+ *
+ * HTML output is used instead of Markdown because:
+ * - <table> tags are unambiguous (no parser variations)
+ * - LLMs are heavily trained on web/HTML data
+ * - Semantic tags (header, footer, section) provide clear structure
+ */
+import { tap, expect } from '@git.zone/tstest/tapbundle';
+import * as fs from 'fs';
+import * as path from 'path';
+import { execSync } from 'child_process';
+import * as os from 'os';
+import { ensurePaddleOcrVlFull, ensureQwen25 } from './helpers/docker.js';
+
+const PADDLEOCR_VL_URL = 'http://localhost:8000';
+const OLLAMA_URL = 'http://localhost:11434';
+// Use Qwen2.5 for text-only JSON extraction (not MiniCPM which is vision-focused)
+const TEXT_MODEL = 'qwen2.5:7b';
+
+interface IInvoice {
+  invoice_number: string;
+  invoice_date: string;
+  vendor_name: string;
+  currency: string;
+  net_amount: number;
+  vat_amount: number;
+  total_amount: number;
+}
+
+/**
+ * Convert PDF to PNG images using ImageMagick
+ */
+function convertPdfToImages(pdfPath: string): string[] {
+  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
+  const outputPattern = path.join(tempDir, 'page-%d.png');
+
+  try {
+    execSync(
+      `convert -density 200 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
+      { stdio: 'pipe' }
+    );
+
+    const files = fs.readdirSync(tempDir).filter((f) => f.endsWith('.png')).sort();
+    const images: string[] = [];
+
+    for (const file of files) {
+      const imagePath = path.join(tempDir, file);
+      const imageData = fs.readFileSync(imagePath);
+      images.push(imageData.toString('base64'));
+    }
+
+    return images;
+  } finally {
+    fs.rmSync(tempDir, { recursive: true, force: true });
+  }
+}
+
+/**
+ * Parse document using PaddleOCR-VL Full Pipeline (returns structured HTML)
+ */
+async function parseDocument(imageBase64: string): Promise<string> {
+  const response = await fetch(`${PADDLEOCR_VL_URL}/parse`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({
+      image: imageBase64,
+      output_format: 'html',
+    }),
+  });
+
+  if (!response.ok) {
+    const text = await response.text();
+    throw new Error(`PaddleOCR-VL API error: ${response.status} - ${text}`);
+  }
+
+  const data = await response.json();
+
+  if (!data.success) {
+    throw new Error(`PaddleOCR-VL error: ${data.error}`);
+  }
+
+  return data.result?.html || '';
+}
+
+/**
+ * Extract invoice fields from structured HTML using Qwen2.5 (text-only model)
+ */
+async function extractInvoiceFromHtml(html: string): Promise<IInvoice> {
+  // Truncate if too long (HTML is more valuable per byte, allow more)
+  const truncated = html.length > 16000 ? html.slice(0, 16000) : html;
+  console.log(`    [Extract] Processing ${truncated.length} chars of HTML`);
+
+  const prompt = `You are an invoice data extractor. Extract the following fields from this HTML document (OCR output with semantic structure) and return ONLY a valid JSON object.
+
+The HTML uses semantic tags:
+- <table> with <thead>/<tbody> for structured tables (invoice line items, totals)
+- <header> for document header (company info, invoice number)
+- <footer> for document footer (payment terms, legal text)
+- <section class="table-region"> for table regions
+- data-type and data-y attributes indicate block type and vertical position
+
+Required fields:
+- invoice_number: The invoice/receipt/document number
+- invoice_date: Date in YYYY-MM-DD format (convert from any format)
+- vendor_name: Company that issued the invoice
+- currency: EUR, USD, GBP, etc.
+- net_amount: Amount before tax (number)
+- vat_amount: Tax/VAT amount (number, use 0 if reverse charge or not shown)
+- total_amount: Final total amount (number)
+
+Example output format:
+{"invoice_number":"INV-123","invoice_date":"2022-01-28","vendor_name":"Adobe","currency":"EUR","net_amount":24.99,"vat_amount":0,"total_amount":24.99}
+
+Rules:
+- Return ONLY the JSON object, no explanation or markdown
+- Use null for missing string fields
+- Use 0 for missing numeric fields
+- Convert dates to YYYY-MM-DD format (e.g., "28-JAN-2022" becomes "2022-01-28")
+- Extract numbers without currency symbols
+- Look for totals in <table> sections, especially rows with "Total", "Amount Due", "Grand Total"
+
+HTML Document:
+${truncated}
+
+JSON:`;
+
+  const payload = {
+    model: TEXT_MODEL,
+    prompt,
+    stream: true,
+    options: {
+      num_predict: 512,
+      temperature: 0.1,
+    },
+  };
+
+  const response = await fetch(`${OLLAMA_URL}/api/generate`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify(payload),
+  });
+
+  if (!response.ok) {
+    throw new Error(`Ollama API error: ${response.status}`);
+  }
+
+  const reader = response.body?.getReader();
+  if (!reader) {
+    throw new Error('No response body');
+  }
+
+  const decoder = new TextDecoder();
+  let fullText = '';
+
+  while (true) {
+    const { done, value } = await reader.read();
+    if (done) break;
+
+    const chunk = decoder.decode(value, { stream: true });
+    const lines = chunk.split('\n').filter((l) => l.trim());
+
+    for (const line of lines) {
+      try {
+        const json = JSON.parse(line);
+        if (json.response) {
+          fullText += json.response;
+        }
+      } catch {
+        // Skip invalid JSON lines
+      }
+    }
+  }
+
+  // Extract JSON from response
+  const startIdx = fullText.indexOf('{');
+  const endIdx = fullText.lastIndexOf('}') + 1;
+
+  if (startIdx < 0 || endIdx <= startIdx) {
+    throw new Error(`No JSON object found in response: ${fullText.substring(0, 200)}`);
+  }
+
+  const jsonStr = fullText.substring(startIdx, endIdx);
+  const parsed = JSON.parse(jsonStr);
+
+  // Ensure numeric fields are actually numbers
+  return {
+    invoice_number: parsed.invoice_number || null,
+    invoice_date: parsed.invoice_date || null,
+    vendor_name: parsed.vendor_name || null,
+    currency: parsed.currency || 'EUR',
+    net_amount: parseFloat(parsed.net_amount) || 0,
+    vat_amount: parseFloat(parsed.vat_amount) || 0,
+    total_amount: parseFloat(parsed.total_amount) || 0,
+  };
+}
+
+/**
+ * Single extraction pass: Parse with PaddleOCR-VL Full, extract with Qwen2.5 (text-only)
+ */
+async function extractOnce(images: string[], passNum: number): Promise<IInvoice> {
+  // Parse document with full pipeline (PaddleOCR-VL) -> returns HTML
+  const html = await parseDocument(images[0]);
+  console.log(`    [Parse] Got ${html.split('\n').length} lines of HTML`);
+
+  // Extract invoice fields from HTML using text-only model (no images)
+  return extractInvoiceFromHtml(html);
+}
+
+/**
+ * Create a hash of invoice for comparison (using key fields)
+ */
+function hashInvoice(invoice: IInvoice): string {
+  // Ensure total_amount is a number
+  const amount = typeof invoice.total_amount === 'number'
+    ? invoice.total_amount.toFixed(2)
+    : String(invoice.total_amount || 0);
+  return `${invoice.invoice_number}|${invoice.invoice_date}|${amount}`;
+}
+
+/**
+ * Extract with consensus voting
+ */
+async function extractWithConsensus(images: string[], invoiceName: string, maxPasses: number = 5): Promise<IInvoice> {
+  const results: Array<{ invoice: IInvoice; hash: string }> = [];
+  const hashCounts: Map<string, number> = new Map();
+
+  const addResult = (invoice: IInvoice, passLabel: string): number => {
+    const hash = hashInvoice(invoice);
+    results.push({ invoice, hash });
+    hashCounts.set(hash, (hashCounts.get(hash) || 0) + 1);
+    console.log(`  [${passLabel}] ${invoice.invoice_number} | ${invoice.invoice_date} | ${invoice.total_amount} ${invoice.currency}`);
+    return hashCounts.get(hash)!;
+  };
+
+  for (let pass = 1; pass <= maxPasses; pass++) {
+    try {
+      const invoice = await extractOnce(images, pass);
+      const count = addResult(invoice, `Pass ${pass}`);
+
+      if (count >= 2) {
+        console.log(`  [Consensus] Reached after ${pass} passes`);
+        return invoice;
+      }
+    } catch (err) {
+      console.log(`  [Pass ${pass}] Error: ${err}`);
+    }
+  }
+
+  // No consensus reached - return the most common result
+  let bestHash = '';
+  let bestCount = 0;
+  for (const [hash, count] of hashCounts) {
+    if (count > bestCount) {
+      bestCount = count;
+      bestHash = hash;
+    }
+  }
+
+  if (!bestHash) {
+    throw new Error(`No valid results for ${invoiceName}`);
+  }
+
+  const best = results.find((r) => r.hash === bestHash)!;
+  console.log(`  [No consensus] Using most common result (${bestCount}/${maxPasses} passes)`);
+  return best.invoice;
+}
+
+/**
+ * Normalize date to YYYY-MM-DD format
+ */
+function normalizeDate(dateStr: string | null): string {
+  if (!dateStr) return '';
+
+  // Already in correct format
+  if (/^\d{4}-\d{2}-\d{2}$/.test(dateStr)) {
+    return dateStr;
+  }
+
+  // Handle DD-MMM-YYYY format (e.g., "28-JUN-2022")
+  const monthMap: Record<string, string> = {
+    JAN: '01', FEB: '02', MAR: '03', APR: '04', MAY: '05', JUN: '06',
+    JUL: '07', AUG: '08', SEP: '09', OCT: '10', NOV: '11', DEC: '12',
+  };
+
+  const match = dateStr.match(/^(\d{1,2})-([A-Z]{3})-(\d{4})$/i);
+  if (match) {
+    const day = match[1].padStart(2, '0');
+    const month = monthMap[match[2].toUpperCase()] || '01';
+    const year = match[3];
+    return `${year}-${month}-${day}`;
+  }
+
+  // Handle DD/MM/YYYY or DD.MM.YYYY
+  const match2 = dateStr.match(/^(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})$/);
+  if (match2) {
+    const day = match2[1].padStart(2, '0');
+    const month = match2[2].padStart(2, '0');
+    const year = match2[3];
+    return `${year}-${month}-${day}`;
+  }
+
+  return dateStr;
+}
+
+/**
+ * Compare extracted invoice against expected
+ */
+function compareInvoice(
+  extracted: IInvoice,
+  expected: IInvoice
+): { match: boolean; errors: string[] } {
+  const errors: string[] = [];
+
+  // Compare invoice number (normalize by removing spaces and case)
+  const extNum = extracted.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
+  const expNum = expected.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
+  if (extNum !== expNum) {
+    errors.push(`invoice_number: expected "${expected.invoice_number}", got "${extracted.invoice_number}"`);
+  }
+
+  // Compare date (normalize format first)
+  const extDate = normalizeDate(extracted.invoice_date);
+  const expDate = normalizeDate(expected.invoice_date);
+  if (extDate !== expDate) {
+    errors.push(`invoice_date: expected "${expected.invoice_date}", got "${extracted.invoice_date}"`);
+  }
+
+  // Compare total amount (with tolerance)
+  if (Math.abs(extracted.total_amount - expected.total_amount) > 0.02) {
+    errors.push(`total_amount: expected ${expected.total_amount}, got ${extracted.total_amount}`);
+  }
+
+  // Compare currency
+  if (extracted.currency?.toUpperCase() !== expected.currency?.toUpperCase()) {
+    errors.push(`currency: expected "${expected.currency}", got "${extracted.currency}"`);
+  }
+
+  return { match: errors.length === 0, errors };
+}
+
+/**
+ * Find all test cases (PDF + JSON pairs) in .nogit/invoices/
+ */
+function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
+  const testDir = path.join(process.cwd(), '.nogit/invoices');
+  if (!fs.existsSync(testDir)) {
+    return [];
+  }
+
+  const files = fs.readdirSync(testDir);
+  const pdfFiles = files.filter((f) => f.endsWith('.pdf'));
+  const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
+
+  for (const pdf of pdfFiles) {
+    const baseName = pdf.replace('.pdf', '');
+    const jsonFile = `${baseName}.json`;
+    if (files.includes(jsonFile)) {
+      testCases.push({
+        name: baseName,
+        pdfPath: path.join(testDir, pdf),
+        jsonPath: path.join(testDir, jsonFile),
+      });
+    }
+  }
+
+  // Sort alphabetically
+  testCases.sort((a, b) => a.name.localeCompare(b.name));
+
+  return testCases;
+}
+
+// Tests
+
+tap.test('setup: ensure Docker containers are running', async () => {
+  console.log('\n[Setup] Checking Docker containers...\n');
+
+  // Ensure PaddleOCR-VL Full Pipeline is running
+  const paddleOk = await ensurePaddleOcrVlFull();
+  expect(paddleOk).toBeTrue();
+
+  // Ensure Qwen2.5 is available (for text-only JSON extraction)
+  const qwenOk = await ensureQwen25();
+  expect(qwenOk).toBeTrue();
+
+  console.log('\n[Setup] All containers ready!\n');
+});
+
+// Dynamic test for each PDF/JSON pair
+const testCases = findTestCases();
+console.log(`\nFound ${testCases.length} invoice test cases (PaddleOCR-VL Full Pipeline)\n`);
+
+let passedCount = 0;
+let failedCount = 0;
+const processingTimes: number[] = [];
+
+for (const testCase of testCases) {
+  tap.test(`should extract invoice: ${testCase.name}`, async () => {
+    // Load expected data
+    const expected: IInvoice = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
+    console.log(`\n=== ${testCase.name} ===`);
+    console.log(`Expected: ${expected.invoice_number} | ${expected.invoice_date} | ${expected.total_amount} ${expected.currency}`);
+
+    const startTime = Date.now();
+
+    // Convert PDF to images
+    const images = convertPdfToImages(testCase.pdfPath);
+    console.log(`  Pages: ${images.length}`);
+
+    // Extract with consensus voting (PaddleOCR-VL Full -> MiniCPM)
+    const extracted = await extractWithConsensus(images, testCase.name);
+
+    const endTime = Date.now();
+    const elapsedMs = endTime - startTime;
+    processingTimes.push(elapsedMs);
+
+    // Compare results
+    const result = compareInvoice(extracted, expected);
+
+    if (result.match) {
+      passedCount++;
+      console.log(`  Result: MATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
+    } else {
+      failedCount++;
+      console.log(`  Result: MISMATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
+      result.errors.forEach((e) => console.log(`    - ${e}`));
+    }
+
+    // Assert match
+    expect(result.match).toBeTrue();
+  });
+}
+
+tap.test('summary', async () => {
+  const totalInvoices = testCases.length;
+  const accuracy = totalInvoices > 0 ? (passedCount / totalInvoices) * 100 : 0;
+  const totalTimeMs = processingTimes.reduce((a, b) => a + b, 0);
+  const avgTimeMs = processingTimes.length > 0 ? totalTimeMs / processingTimes.length : 0;
+  const avgTimeSec = avgTimeMs / 1000;
+  const totalTimeSec = totalTimeMs / 1000;
+
+  console.log(`\n======================================================`);
+  console.log(`   Invoice Extraction Summary (PaddleOCR-VL Full)`);
+  console.log(`======================================================`);
+  console.log(`  Method:    PaddleOCR-VL Full Pipeline (HTML) -> Qwen2.5 (text-only)`);
+  console.log(`  Passed:    ${passedCount}/${totalInvoices}`);
+  console.log(`  Failed:    ${failedCount}/${totalInvoices}`);
+  console.log(`  Accuracy:  ${accuracy.toFixed(1)}%`);
+  console.log(`------------------------------------------------------`);
+  console.log(`  Total time:   ${totalTimeSec.toFixed(1)}s`);
+  console.log(`  Avg per inv:  ${avgTimeSec.toFixed(1)}s`);
+  console.log(`======================================================\n`);
+});
+
+export default tap.start();
Author	SHA1	Message	Date
Juergen Kunz	b316d98f24	v1.8.0 Some checks failed Docker (tags) / security (push) Successful in 31s Details Docker (tags) / test (push) Failing after 41s Details Docker (tags) / release (push) Has been skipped Details Docker (tags) / metadata (push) Has been skipped Details	2026-01-18 00:11:17 +00:00
Juergen Kunz	f0d88fcbe0	feat(paddleocr-vl): add structured HTML output and table parsing for PaddleOCR-VL, update API, tests, and README	2026-01-18 00:11:17 +00:00
Juergen Kunz	0d8a1ebac2	v1.7.1 Some checks failed Docker (tags) / security (push) Successful in 31s Details Docker (tags) / test (push) Failing after 39s Details Docker (tags) / release (push) Has been skipped Details Docker (tags) / metadata (push) Has been skipped Details	2026-01-17 23:13:47 +00:00
Juergen Kunz	5a311dca2d	fix(docker): standardize Dockerfile and entrypoint filenames; add GPU-specific Dockerfiles and update build and test references	2026-01-17 23:13:47 +00:00
Juergen Kunz	ab288380f1	v1.7.0 Some checks failed Docker (tags) / security (push) Successful in 30s Details Docker (tags) / test (push) Failing after 40s Details Docker (tags) / release (push) Has been skipped Details Docker (tags) / metadata (push) Has been skipped Details	2026-01-17 21:50:09 +00:00
Juergen Kunz	30c73b24c1	feat(tests): use Qwen2.5 (Ollama) for invoice extraction tests and add helpers for model management; normalize dates and coerce numeric fields	2026-01-17 21:50:09 +00:00
Juergen Kunz	311e7a8fd4	v1.6.0 Some checks failed Docker (tags) / security (push) Successful in 32s Details Docker (tags) / test (push) Failing after 40s Details Docker (tags) / release (push) Has been skipped Details Docker (tags) / metadata (push) Has been skipped Details	2026-01-17 20:22:23 +00:00
Juergen Kunz	80e6866442	feat(paddleocr-vl): add PaddleOCR-VL full pipeline Docker image and API server, plus integration tests and docker helpers	2026-01-17 20:22:23 +00:00