feat(ocr): add PaddleOCR GPU Docker image and FastAPI OCR server with entrypoint; implement OCR endpoints and consensus extraction testing

2026-01-16 10:22:15 +00:00
parent 3dc1881d8b
commit 379b5c19eb
7 changed files with 847 additions and 14 deletions
--- a/51
+++ b/51
@@ -0,0 +1,51 @@
+# PaddleOCR GPU Variant
+# OCR processing with NVIDIA GPU support using PaddlePaddle
+FROM paddlepaddle/paddle:3.0.0-gpu-cuda11.8-cudnn8.9-trt8.6
+
+LABEL maintainer="Task Venture Capital GmbH <hello@task.vc>"
+LABEL description="PaddleOCR PP-OCRv4 - GPU optimized"
+LABEL org.opencontainers.image.source="https://code.foss.global/host.today/ht-docker-ai"
+
+# Environment configuration
+ENV OCR_LANGUAGE="en"
+ENV SERVER_PORT="5000"
+ENV SERVER_HOST="0.0.0.0"
+ENV PYTHONUNBUFFERED=1
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Python dependencies
+RUN pip install --no-cache-dir \
+    paddleocr \
+    fastapi \
+    uvicorn[standard] \
+    python-multipart \
+    opencv-python-headless \
+    pillow
+
+# Copy server files
+COPY image_support_files/paddleocr-server.py /app/paddleocr-server.py
+COPY image_support_files/paddleocr-entrypoint.sh /usr/local/bin/paddleocr-entrypoint.sh
+RUN chmod +x /usr/local/bin/paddleocr-entrypoint.sh
+
+# Pre-download OCR models during build (PP-OCRv4)
+RUN python -c "from paddleocr import PaddleOCR; \
+    ocr = PaddleOCR(use_angle_cls=True, lang='en', use_gpu=False, show_log=True); \
+    print('English model downloaded')"
+
+# Expose API port
+EXPOSE 5000
+
+# Health check
+HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
+    CMD curl -f http://localhost:5000/health || exit 1
+
+ENTRYPOINT ["/usr/local/bin/paddleocr-entrypoint.sh"]
--- a/54
+++ b/54
@@ -0,0 +1,54 @@
+# PaddleOCR CPU Variant
+# OCR processing optimized for CPU-only inference
+FROM python:3.10-slim
+
+LABEL maintainer="Task Venture Capital GmbH <hello@task.vc>"
+LABEL description="PaddleOCR PP-OCRv4 - CPU optimized"
+LABEL org.opencontainers.image.source="https://code.foss.global/host.today/ht-docker-ai"
+
+# Environment configuration for CPU-only mode
+ENV OCR_LANGUAGE="en"
+ENV SERVER_PORT="5000"
+ENV SERVER_HOST="0.0.0.0"
+ENV PYTHONUNBUFFERED=1
+# Disable GPU usage for CPU-only variant
+ENV CUDA_VISIBLE_DEVICES="-1"
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Python dependencies (CPU version of PaddlePaddle)
+RUN pip install --no-cache-dir \
+    paddlepaddle \
+    paddleocr \
+    fastapi \
+    uvicorn[standard] \
+    python-multipart \
+    opencv-python-headless \
+    pillow
+
+# Copy server files
+COPY image_support_files/paddleocr-server.py /app/paddleocr-server.py
+COPY image_support_files/paddleocr-entrypoint.sh /usr/local/bin/paddleocr-entrypoint.sh
+RUN chmod +x /usr/local/bin/paddleocr-entrypoint.sh
+
+# Pre-download OCR models during build (PP-OCRv4)
+RUN python -c "from paddleocr import PaddleOCR; \
+    ocr = PaddleOCR(use_angle_cls=True, lang='en', use_gpu=False, show_log=True); \
+    print('English model downloaded')"
+
+# Expose API port
+EXPOSE 5000
+
+# Health check (longer start-period for CPU variant)
+HEALTHCHECK --interval=30s --timeout=10s --start-period=120s --retries=3 \
+    CMD curl -f http://localhost:5000/health || exit 1
+
+ENTRYPOINT ["/usr/local/bin/paddleocr-entrypoint.sh"]
--- a/changelog.md
+++ b/changelog.md
@@ -0,0 +1,16 @@
+# Changelog
+
+## 2026-01-16 - 1.1.0 - feat(ocr)
+add PaddleOCR GPU Docker image and FastAPI OCR server with entrypoint; implement OCR endpoints and consensus extraction testing
+
+- Add Dockerfile_paddleocr for GPU-accelerated PaddleOCR image (pre-downloads PP-OCRv4 models, exposes port 5000, healthcheck, entrypoint)
+- Add image_support_files/paddleocr-server.py: FastAPI app providing /ocr (base64), /ocr/upload (file), and /health endpoints; model warm-up on startup; structured JSON responses and error handling
+- Add image_support_files/paddleocr-entrypoint.sh to configure environment, detect GPU/CPU mode, and launch uvicorn
+- Update test/test.node.ts to replace streaming extraction with a consensus-based extraction flow (multiple passes, hashing of results, majority voting) and improve logging/prompt text
+- Add test/test.invoices.ts: integration tests for invoice extraction that call PaddleOCR, build prompts with optional OCR text, run consensus extraction, and produce a summary report
+
+## 2026-01-16 - 1.0.0 - initial release
+Initial project files added with two small follow-up updates.
+
+- initial: base project commit.
+- update: two minor follow-up updates refining the initial commit.
--- a/image_support_files/paddleocr-entrypoint.sh
+++ b/image_support_files/paddleocr-entrypoint.sh
@@ -0,0 +1,25 @@
+#!/bin/bash
+set -e
+
+# Configuration from environment
+OCR_LANGUAGE="${OCR_LANGUAGE:-en}"
+SERVER_PORT="${SERVER_PORT:-5000}"
+SERVER_HOST="${SERVER_HOST:-0.0.0.0}"
+
+echo "Starting PaddleOCR Server..."
+echo "  Language: ${OCR_LANGUAGE}"
+echo "  Host: ${SERVER_HOST}"
+echo "  Port: ${SERVER_PORT}"
+
+# Check GPU availability
+if [ "${CUDA_VISIBLE_DEVICES}" = "-1" ]; then
+    echo "  GPU: Disabled (CPU mode)"
+else
+    echo "  GPU: Enabled"
+fi
+
+# Start the FastAPI server with uvicorn
+exec python -m uvicorn paddleocr-server:app \
+    --host "${SERVER_HOST}" \
+    --port "${SERVER_PORT}" \
+    --workers 1
--- a/image_support_files/paddleocr-server.py
+++ b/image_support_files/paddleocr-server.py
@@ -0,0 +1,258 @@
+#!/usr/bin/env python3
+"""
+PaddleOCR FastAPI Server
+Provides REST API for OCR operations using PaddleOCR
+"""
+
+import os
+import io
+import base64
+import logging
+from typing import Optional, List, Any
+
+from fastapi import FastAPI, File, UploadFile, Form, HTTPException
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel
+import numpy as np
+from PIL import Image
+from paddleocr import PaddleOCR
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+# Environment configuration
+OCR_LANGUAGE = os.environ.get('OCR_LANGUAGE', 'en')
+USE_GPU = os.environ.get('CUDA_VISIBLE_DEVICES', '') != '-1'
+
+# Initialize FastAPI app
+app = FastAPI(
+    title="PaddleOCR Server",
+    description="REST API for OCR operations using PaddleOCR PP-OCRv4",
+    version="1.0.0"
+)
+
+# Global OCR instance
+ocr_instance: Optional[PaddleOCR] = None
+
+
+class OCRRequest(BaseModel):
+    """Request model for base64 image OCR"""
+    image: str
+    language: Optional[str] = None
+
+
+class BoundingBox(BaseModel):
+    """Bounding box for detected text"""
+    points: List[List[float]]
+
+
+class OCRResult(BaseModel):
+    """Single OCR detection result"""
+    text: str
+    confidence: float
+    box: List[List[float]]
+
+
+class OCRResponse(BaseModel):
+    """OCR response model"""
+    success: bool
+    results: List[OCRResult]
+    error: Optional[str] = None
+
+
+class HealthResponse(BaseModel):
+    """Health check response"""
+    status: str
+    model: str
+    language: str
+    gpu_enabled: bool
+
+
+def get_ocr() -> PaddleOCR:
+    """Get or initialize the OCR instance"""
+    global ocr_instance
+    if ocr_instance is None:
+        logger.info(f"Initializing PaddleOCR with language={OCR_LANGUAGE}, use_gpu={USE_GPU}")
+        ocr_instance = PaddleOCR(
+            use_angle_cls=True,
+            lang=OCR_LANGUAGE,
+            use_gpu=USE_GPU,
+            show_log=False
+        )
+        logger.info("PaddleOCR initialized successfully")
+    return ocr_instance
+
+
+def decode_base64_image(base64_string: str) -> np.ndarray:
+    """Decode base64 string to numpy array"""
+    # Remove data URL prefix if present
+    if ',' in base64_string:
+        base64_string = base64_string.split(',')[1]
+
+    image_data = base64.b64decode(base64_string)
+    image = Image.open(io.BytesIO(image_data))
+
+    # Convert to RGB if necessary
+    if image.mode != 'RGB':
+        image = image.convert('RGB')
+
+    return np.array(image)
+
+
+def process_ocr_result(result: Any) -> List[OCRResult]:
+    """Process PaddleOCR result into structured format"""
+    results = []
+
+    if result is None or len(result) == 0:
+        return results
+
+    # PaddleOCR returns list of results per image
+    # Each result is a list of [box, (text, confidence)]
+    for line in result[0] if result[0] else []:
+        if line is None:
+            continue
+
+        box = line[0]  # [[x1,y1], [x2,y2], [x3,y3], [x4,y4]]
+        text_info = line[1]  # (text, confidence)
+
+        results.append(OCRResult(
+            text=text_info[0],
+            confidence=float(text_info[1]),
+            box=[[float(p[0]), float(p[1])] for p in box]
+        ))
+
+    return results
+
+
+@app.on_event("startup")
+async def startup_event():
+    """Pre-warm the OCR model on startup"""
+    logger.info("Pre-warming OCR model...")
+    try:
+        ocr = get_ocr()
+        # Create a small test image to warm up the model
+        test_image = np.zeros((100, 100, 3), dtype=np.uint8)
+        test_image.fill(255)  # White image
+        ocr.ocr(test_image, cls=True)
+        logger.info("OCR model pre-warmed successfully")
+    except Exception as e:
+        logger.error(f"Failed to pre-warm OCR model: {e}")
+
+
+@app.get("/health", response_model=HealthResponse)
+async def health_check():
+    """Health check endpoint"""
+    try:
+        # Ensure OCR is initialized
+        get_ocr()
+        return HealthResponse(
+            status="healthy",
+            model="PP-OCRv4",
+            language=OCR_LANGUAGE,
+            gpu_enabled=USE_GPU
+        )
+    except Exception as e:
+        logger.error(f"Health check failed: {e}")
+        raise HTTPException(status_code=503, detail=str(e))
+
+
+@app.post("/ocr", response_model=OCRResponse)
+async def ocr_base64(request: OCRRequest):
+    """
+    Perform OCR on a base64-encoded image
+
+    Args:
+        request: OCRRequest with base64 image and optional language
+
+    Returns:
+        OCRResponse with detected text, confidence scores, and bounding boxes
+    """
+    try:
+        # Decode image
+        image = decode_base64_image(request.image)
+
+        # Get OCR instance (use request language if provided)
+        ocr = get_ocr()
+
+        # If a different language is requested, create a new instance
+        if request.language and request.language != OCR_LANGUAGE:
+            logger.info(f"Creating OCR instance for language: {request.language}")
+            temp_ocr = PaddleOCR(
+                use_angle_cls=True,
+                lang=request.language,
+                use_gpu=USE_GPU,
+                show_log=False
+            )
+            result = temp_ocr.ocr(image, cls=True)
+        else:
+            result = ocr.ocr(image, cls=True)
+
+        # Process results
+        results = process_ocr_result(result)
+
+        return OCRResponse(success=True, results=results)
+
+    except Exception as e:
+        logger.error(f"OCR processing failed: {e}")
+        return OCRResponse(success=False, results=[], error=str(e))
+
+
+@app.post("/ocr/upload", response_model=OCRResponse)
+async def ocr_upload(
+    img: UploadFile = File(...),
+    language: Optional[str] = Form(None)
+):
+    """
+    Perform OCR on an uploaded image file
+
+    Args:
+        img: Uploaded image file
+        language: Optional language code (default: env OCR_LANGUAGE)
+
+    Returns:
+        OCRResponse with detected text, confidence scores, and bounding boxes
+    """
+    try:
+        # Read image
+        contents = await img.read()
+        image = Image.open(io.BytesIO(contents))
+
+        # Convert to RGB if necessary
+        if image.mode != 'RGB':
+            image = image.convert('RGB')
+
+        image_array = np.array(image)
+
+        # Get OCR instance
+        ocr = get_ocr()
+
+        # If a different language is requested, create a new instance
+        if language and language != OCR_LANGUAGE:
+            logger.info(f"Creating OCR instance for language: {language}")
+            temp_ocr = PaddleOCR(
+                use_angle_cls=True,
+                lang=language,
+                use_gpu=USE_GPU,
+                show_log=False
+            )
+            result = temp_ocr.ocr(image_array, cls=True)
+        else:
+            result = ocr.ocr(image_array, cls=True)
+
+        # Process results
+        results = process_ocr_result(result)
+
+        return OCRResponse(success=True, results=results)
+
+    except Exception as e:
+        logger.error(f"OCR processing failed: {e}")
+        return OCRResponse(success=False, results=[], error=str(e))
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=5000)
--- a/test/test.invoices.ts
+++ b/test/test.invoices.ts
@@ -0,0 +1,377 @@
+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';
+
+const OLLAMA_URL = 'http://localhost:11434';
+const MODEL = 'openbmb/minicpm-v4.5:q8_0';
+const PADDLEOCR_URL = 'http://localhost:5000';
+
+interface IInvoice {
+  invoice_number: string;
+  invoice_date: string;
+  vendor_name: string;
+  currency: string;
+  net_amount: number;
+  vat_amount: number;
+  total_amount: number;
+}
+
+/**
+ * Extract OCR text from an image using PaddleOCR
+ */
+async function extractOcrText(imageBase64: string): Promise<string> {
+  const formData = new FormData();
+  const imageBuffer = Buffer.from(imageBase64, 'base64');
+  const blob = new Blob([imageBuffer], { type: 'image/png' });
+  formData.append('img', blob, 'image.png');
+  formData.append('outtype', 'json');
+
+  try {
+    const response = await fetch(`${PADDLEOCR_URL}/ocr`, {
+      method: 'POST',
+      body: formData,
+    });
+
+    if (!response.ok) return '';
+
+    const data = await response.json();
+    if (data.success && data.results) {
+      return data.results.map((r: { text: string }) => r.text).join('\n');
+    }
+  } catch {
+    // PaddleOCR unavailable
+  }
+  return '';
+}
+
+/**
+ * Build prompt with optional OCR text
+ */
+function buildPrompt(ocrText: string): string {
+  const base = `You are an invoice parser. Extract the following fields from this invoice:
+
+1. invoice_number: The invoice/receipt number
+2. invoice_date: Date in YYYY-MM-DD format
+3. vendor_name: Company that issued the invoice
+4. currency: EUR, USD, etc.
+5. net_amount: Amount before tax (if shown)
+6. vat_amount: Tax/VAT amount (if shown, 0 if reverse charge or no tax)
+7. total_amount: Final amount due
+
+Return ONLY valid JSON in this exact format:
+{"invoice_number":"XXX","invoice_date":"YYYY-MM-DD","vendor_name":"Company Name","currency":"EUR","net_amount":100.00,"vat_amount":19.00,"total_amount":119.00}
+
+If a field is not visible, use null for strings or 0 for numbers.
+No explanation, just the JSON object.`;
+
+  if (ocrText) {
+    return `${base}
+
+OCR text extracted from the invoice:
+---
+${ocrText}
+---
+
+Cross-reference the image with the OCR text above for accuracy.`;
+  }
+  return base;
+}
+
+/**
+ * Convert PDF to PNG images using ImageMagick
+ */
+function convertPdfToImages(pdfPath: string): string[] {
+  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
+  const outputPattern = path.join(tempDir, 'page-%d.png');
+
+  try {
+    execSync(
+      `convert -density 200 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
+      { stdio: 'pipe' }
+    );
+
+    const files = fs.readdirSync(tempDir).filter((f) => f.endsWith('.png')).sort();
+    const images: string[] = [];
+
+    for (const file of files) {
+      const imagePath = path.join(tempDir, file);
+      const imageData = fs.readFileSync(imagePath);
+      images.push(imageData.toString('base64'));
+    }
+
+    return images;
+  } finally {
+    fs.rmSync(tempDir, { recursive: true, force: true });
+  }
+}
+
+/**
+ * Single extraction pass
+ */
+async function extractOnce(images: string[], passNum: number, ocrText: string = ''): Promise<IInvoice> {
+  const payload = {
+    model: MODEL,
+    prompt: buildPrompt(ocrText),
+    images,
+    stream: true,
+    options: {
+      num_predict: 2048,
+      temperature: 0.1,
+    },
+  };
+
+  const response = await fetch(`${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 majority voting - run until 2 passes match
+ */
+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();
+
+  // Extract OCR text from first page
+  const ocrText = await extractOcrText(images[0]);
+  if (ocrText) {
+    console.log(`  [OCR] Extracted ${ocrText.split('\n').length} text lines`);
+  }
+
+  for (let pass = 1; pass <= maxPasses; pass++) {
+    try {
+      const invoice = await extractOnce(images, pass, ocrText);
+      const hash = hashInvoice(invoice);
+
+      results.push({ invoice, hash });
+      hashCounts.set(hash, (hashCounts.get(hash) || 0) + 1);
+
+      console.log(`  [Pass ${pass}] ${invoice.invoice_number} | ${invoice.invoice_date} | ${invoice.total_amount} ${invoice.currency}`);
+
+      // Check if we have consensus (2+ matching)
+      const count = hashCounts.get(hash)!;
+      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),
+      });
+    }
+  }
+
+  return testCases;
+}
+
+// Tests
+
+tap.test('should connect to Ollama API', async () => {
+  const response = await fetch(`${OLLAMA_URL}/api/tags`);
+  expect(response.ok).toBeTrue();
+  const data = await response.json();
+  expect(data.models).toBeArray();
+});
+
+tap.test('should have MiniCPM-V 4.5 model loaded', async () => {
+  const response = await fetch(`${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\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: ${images.length}`);
+
+    // Extract with consensus voting
+    const extracted = await extractWithConsensus(images, testCase.name);
+
+    const endTime = Date.now();
+    const elapsedMs = endTime - startTime;
+    processingTimes.push(elapsedMs);
+
+    // Compare results
+    const result = compareInvoice(extracted, expected);
+
+    if (result.match) {
+      passedCount++;
+      console.log(`  Result: MATCH (${(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`);
+  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.node.ts
+++ b/test/test.node.ts
@@ -7,7 +7,7 @@ import * as os from 'os';
 const OLLAMA_URL = 'http://localhost:11434';
 const MODEL = 'openbmb/minicpm-v4.5:q8_0';

-const BANK_STATEMENT_PROMPT = `You are a bank statement parser. Extract EVERY transaction from the table.
+const EXTRACT_PROMPT = `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
@@ -16,7 +16,7 @@ Read the Amount column carefully:

 For each row output: {"date":"YYYY-MM-DD","counterparty":"NAME","amount":-21.47}

-Do not skip any rows. Return complete JSON array:`;
+Do not skip any rows. Return ONLY the JSON array, no explanation.`;

 interface ITransaction {
  date: string;
@@ -53,12 +53,12 @@ function convertPdfToImages(pdfPath: string): string[] {
 }

 /**
- * Extract transactions from images using Ollama with streaming
+ * Single extraction pass
 */
-async function extractTransactionsStreaming(images: string[]): Promise<ITransaction[]> {
+async function extractOnce(images: string[], passNum: number): Promise<ITransaction[]> {
  const payload = {
    model: MODEL,
-    prompt: BANK_STATEMENT_PROMPT,
+    prompt: EXTRACT_PROMPT,
    images,
    stream: true,
    options: {
@@ -86,7 +86,8 @@ async function extractTransactionsStreaming(images: string[]): Promise<ITransact
  let fullText = '';
  let lineBuffer = '';

-  // Stream and print output (buffer until newline for cleaner display)
+  console.log(`[Pass ${passNum}] Extracting...`);
+
  while (true) {
    const { done, value } = await reader.read();
    if (done) break;
@@ -116,13 +117,11 @@ async function extractTransactionsStreaming(images: string[]): Promise<ITransact
    }
  }

-  // Print any remaining buffer
  if (lineBuffer) {
    console.log(lineBuffer);
  }
  console.log('');

-  // Parse JSON from response
  const startIdx = fullText.indexOf('[');
  const endIdx = fullText.lastIndexOf(']') + 1;

@@ -133,6 +132,60 @@ async function extractTransactionsStreaming(images: string[]): Promise<ITransact
  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 majority voting - run until 2 passes match
+ */
+async function extractWithConsensus(images: string[], maxPasses: number = 5): Promise<ITransaction[]> {
+  const results: Array<{ transactions: ITransaction[]; hash: string }> = [];
+  const hashCounts: Map<string, number> = new Map();
+
+  for (let pass = 1; pass <= maxPasses; pass++) {
+    const transactions = await extractOnce(images, pass);
+    const hash = hashTransactions(transactions);
+
+    results.push({ transactions, hash });
+    hashCounts.set(hash, (hashCounts.get(hash) || 0) + 1);
+
+    console.log(`[Pass ${pass}] Got ${transactions.length} transactions (hash: ${hash.substring(0, 20)}...)`);
+
+    // Check if we have consensus (2+ matching)
+    const count = hashCounts.get(hash)!;
+    if (count >= 2) {
+      console.log(`[Consensus] Reached after ${pass} passes (${count} matching results)`);
+      return transactions;
+    }
+
+    // After 2 passes, if no match yet, continue
+    if (pass >= 2) {
+      console.log(`[Pass ${pass}] No consensus yet, trying again...`);
+    }
+  }
+
+  // No consensus reached - return the most common result
+  let bestHash = '';
+  let bestCount = 0;
+  for (const [hash, count] of hashCounts) {
+    if (count > bestCount) {
+      bestCount = count;
+      bestHash = hash;
+    }
+  }
+
+  const best = results.find((r) => r.hash === bestHash)!;
+  console.log(`[No consensus] Using most common result (${bestCount}/${maxPasses} passes)`);
+  return best.transactions;
+}
+
 /**
 * Compare extracted transactions against expected
 */
@@ -227,16 +280,15 @@ for (const testCase of testCases) {
    // Convert PDF to images
    console.log('Converting PDF to images...');
    const images = convertPdfToImages(testCase.pdfPath);
-    console.log(`Converted: ${images.length} pages`);
+    console.log(`Converted: ${images.length} pages\n`);

-    // Extract transactions with streaming output
-    console.log('Extracting transactions (streaming)...\n');
-    const extracted = await extractTransactionsStreaming(images);
-    console.log(`Extracted: ${extracted.length} transactions`);
+    // Extract with consensus voting
+    const extracted = await extractWithConsensus(images);
+    console.log(`\nFinal: ${extracted.length} transactions`);

    // Compare results
    const result = compareTransactions(extracted, expected);
-    console.log(`Matches: ${result.matches}/${result.total}`);
+    console.log(`Accuracy: ${result.matches}/${result.total}`);

    if (result.errors.length > 0) {
      console.log('Errors:');