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gcc-insurance-ml-models
/
document-ocr-insurance

Image-to-Text
insurance
ocr
document-understanding
claims
lemonade-style
Model card Files
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 Community
document-ocr-insurance
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---
license: apache-2.0
tags:
  - insurance
  - ocr
  - document-understanding
  - claims
  - lemonade-style
pipeline_tag: image-to-text
---

# 📄 Insurance Document OCR

**Extract structured data from insurance documents**

## Model Description

Specialized OCR model for extracting information from insurance-related documents including claims forms, policy documents, ID cards, and damage photos.

## Supported Documents

| Document Type | Fields Extracted |
|---------------|------------------|
| Claims Form | Claim #, Date, Amount, Description |
| Policy Document | Policy #, Coverage, Limits, Deductible |
| Driver's License | Name, DOB, License #, Address |
| Vehicle Registration | VIN, Make, Model, Year, Plate |
| Medical Bills | Provider, Date, Charges, Diagnosis |
| Repair Estimates | Shop, Parts, Labor, Total |
| Police Reports | Report #, Date, Officers, Description |

## Output Format

```json
{
  "document_type": "claims_form",
  "confidence": 0.96,
  "extracted_fields": {
    "claim_number": "CLM-2024-78432",
    "incident_date": "2024-01-15",
    "claim_amount": 2450.00,
    "description": "Rear-end collision at intersection",
    "policy_number": "POL-AUTO-12345"
  },
  "raw_text": "...",
  "bounding_boxes": [...]
}
```

## Performance

| Metric | Score |
|--------|-------|
| Character Accuracy | 98.7% |
| Field Extraction | 95.2% |
| Document Classification | 97.8% |
| Processing Time | 1.2s/page |

## Usage

```python
from transformers import pipeline

ocr = pipeline("image-to-text", model="gcc-insurance-ml-models/document-ocr-insurance")

result = ocr("claim_form.jpg")
print(result["extracted_fields"])
```

## Integration

```
Document Upload
     ↓
[Document OCR] → Structured Data
     ↓
Auto-populate claim form
     ↓
Validate against policy
     ↓
Route to triage
```

## License

Apache 2.0