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+# Bank Statement Parsing with MiniCPM-V 4.5
+
+Recipe for extracting transactions from bank statement PDFs using vision-language AI.
+
+## Model
+
+- **Model**: MiniCPM-V 4.5 (8B parameters)
+- **Ollama Name**: `openbmb/minicpm-v4.5:q8_0`
+- **Quantization**: Q8_0 (9.8GB VRAM)
+- **Runtime**: Ollama on GPU
+
+## Image Conversion
+
+Convert PDF to PNG at 300 DPI for optimal OCR accuracy.
+
+```bash
+convert -density 300 -quality 100 input.pdf \
+  -background white -alpha remove \
+  output-%d.png
+```
+
+**Parameters:**
+- `-density 300`: 300 DPI resolution (critical for accuracy)
+- `-quality 100`: Maximum quality
+- `-background white -alpha remove`: Remove transparency
+- `output-%d.png`: Outputs page-0.png, page-1.png, etc.
+
+**Dependencies:**
+```bash
+apt-get install imagemagick
+```
+
+## 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
+- "+ 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 complete JSON array:
+```
+
+## API Call
+
+```python
+import base64
+import requests
+
+# Load images
+with open('page-0.png', 'rb') as f:
+    page0 = base64.b64encode(f.read()).decode('utf-8')
+with open('page-1.png', 'rb') as f:
+    page1 = base64.b64encode(f.read()).decode('utf-8')
+
+payload = {
+    "model": "openbmb/minicpm-v4.5:q8_0",
+    "prompt": prompt,
+    "images": [page0, page1],  # Multiple pages supported
+    "stream": False,
+    "options": {
+        "num_predict": 16384,
+        "temperature": 0.1
+    }
+}
+
+response = requests.post(
+    'http://localhost:11434/api/generate',
+    json=payload,
+    timeout=600
+)
+
+result = response.json()['response']
+```
+
+## Output Format
+
+```json
+[
+  {"date":"2022-04-01","counterparty":"DIGITALOCEAN.COM","amount":-21.47},
+  {"date":"2022-04-01","counterparty":"DIGITALOCEAN.COM","amount":-58.06},
+  {"date":"2022-04-12","counterparty":"LOSSLESS GMBH","amount":1000.00}
+]
+```
+
+## Running the Container
+
+**GPU (recommended):**
+```bash
+docker run -d --gpus all -p 11434:11434 \
+  -v ollama-data:/root/.ollama \
+  -e MODEL_NAME="openbmb/minicpm-v4.5:q8_0" \
+  ht-docker-ai:minicpm45v
+```
+
+**CPU (slower):**
+```bash
+docker run -d -p 11434:11434 \
+  -v ollama-data:/root/.ollama \
+  -e MODEL_NAME="openbmb/minicpm-v4.5:q4_0" \
+  ht-docker-ai:minicpm45v-cpu
+```
+
+## Hardware Requirements
+
+| Quantization | VRAM/RAM | Speed |
+|--------------|----------|-------|
+| Q8_0 (GPU)   | 10GB     | Fast  |
+| Q4_0 (CPU)   | 8GB      | Slow  |
+
+## Test Results
+
+| Statement | Pages | Transactions | Accuracy |
+|-----------|-------|--------------|----------|
+| bunq-2022-04 | 2 | 26 | 100% |
+| bunq-2021-06 | 3 | 28 | 100% |
+
+## Tips
+
+1. **DPI matters**: 150 DPI causes missed rows; 300 DPI is optimal
+2. **PNG over JPEG**: PNG preserves text clarity better
+3. **Remove alpha**: Some models struggle with transparency
+4. **Multi-page**: Pass all pages in single request for context
+5. **Temperature 0.1**: Low temperature for consistent output
+6. **European format**: Explicitly explain comma=decimal in prompt