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data/task/cni/inference_out/SUMMARY.md

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+# Evaluation Results Summary
+
+## Quick Overview
+- **Dataset**: 56 document samples
+- **Best Approach**: Crop (No Shadow Removal)
+- **Performance Gain**: +14.1% F1-score improvement over baseline
+
+## Performance Comparison (Ranked from Lowest to Highest)
+
+| Approach | Precision | Recall | F1-Score | Field Accuracy | Improvement vs. Baseline |
+|----------|-----------|--------|----------|----------------|---------------------------|
+| **No Preprocessing** | 79.0% | 68.7% | 73.5% | 68.7% | Baseline |
+| **Crop + PaddleOCR + Shadow Removal + Cache** | 92.5% | 88.3% | 90.3% | 88.3% | +16.8% |
+| **Crop + Shadow Removal + Cache** | 93.6% | 88.5% | 91.0% | 88.5% | +17.5% |
+| **Crop + PaddleOCR + Shadow Removal** | 93.6% | 89.4% | 91.5% | 89.4% | +18.0% |
+| **Crop** | 94.8% | 89.9% | 92.3% | 89.9% | +18.8% |
+
+## Top Performing Fields
+- **Gender**: 85.1% F1 (Crop + PaddleOCR + Shadow Removal)
+- **Birth Date**: 80.5% F1 (Crop + PaddleOCR + Shadow Removal)
+- **Document Type**: 85.4% F1 (Crop + PaddleOCR + Shadow Removal)
+- **Surname**: 82.9% F1 (Crop + PaddleOCR + Shadow Removal)
+
+## Key Insights
+1. **Cropping** provides the biggest performance boost
+2. **PaddleOCR + Shadow Removal** adds small but consistent improvement
+3. **Shadow removal** shows mixed results depending on field type
+4. **Caching** has minimal impact on accuracy
+
+## Recommendations
+- Use **Crop + PaddleOCR + Shadow Removal** for production
+- Focus on optimizing high-value fields
+- Investigate MRZ line extraction further
+- Target 65%+ overall F1-score
+
+---
+*See README.md for detailed analysis*