Add OCR benchmark test feature

[squarezw]

Summary

Implemented a comprehensive OCR benchmark testing framework that can test different OCR open source SDKs.

Features

• ✅ Support for PaddleOCR with Chinese/English language support
• ✅ Support for Tesseract OCR with multiple languages
• ✅ Extensible architecture for DeepSeek OCR and custom models
• ✅ Benchmark framework with performance metrics
• ✅ Ground truth comparison for accuracy measurement
• ✅ Batch processing support
• ✅ Model registry for easy access
• ✅ Comprehensive documentation and examples

Files Added

• src/ragent_lab/ocr_benchmark/: Core OCR benchmark module
• docs/ocr_benchmark.md: Detailed documentation
• examples/ocr_benchmark_example.py: Usage examples
• tests/test_ocr_benchmark.py: Test suite
• requirements-ocr.txt: Optional OCR dependencies

Testing

The implementation includes:

• Unit tests for base classes
• Registry tests
• Benchmark framework tests
• Example scripts demonstrating usage

Resolves #4

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Generated with Claude Code

[claude]

Pull Request Review: OCR Benchmark Feature

Summary

This PR adds a comprehensive OCR benchmark testing framework with support for multiple OCR engines. The implementation follows good architectural patterns and provides a solid foundation for OCR testing. Overall, this is well-structured code with good documentation.

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✅ Strengths

1. Excellent Architecture: Clean separation of concerns with base classes, strategies, registry pattern, and benchmark framework
2. Comprehensive Documentation: The docs/ocr_benchmark.md file is thorough and includes examples, troubleshooting, and API reference
3. Good Test Coverage: Unit tests cover base functionality, registry, and mocking for external dependencies
4. Extensibility: The registry pattern makes it easy to add new OCR models
5. Type Hints: Good use of type annotations throughout the codebase
6. Error Handling: Proper use of try-except blocks with informative error messages

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🐛 Issues & Bugs

High Priority

1. Resource Leak Potential

• Location: src/ragent_lab/ocr_benchmark/strategies.py:167
• Issue: Image.open(image_path) is not closed explicitly
• Recommendation: Use context manager to ensure proper resource cleanup

2. Import Location

• Location: src/ragent_lab/ocr_benchmark/benchmark.py:204
• Issue: from difflib import SequenceMatcher is imported inside a method
• Impact: This works but is unconventional and hurts performance
• Recommendation: Move this import to the top of the file

3. Unused Import

• Location: src/ragent_lab/ocr_benchmark/base.py:9
• Issue: import time is never used in this file
• Recommendation: Remove unused import

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⚠️ Code Quality & Best Practices

1. Print Statements in Library Code

• Location: Multiple locations in benchmark.py (lines 34, 129, 136)
• Issue: Using print() directly in library code
• Recommendation: Consider using Python's logging module instead for better control

2. Magic Numbers

• Location: src/ragent_lab/ocr_benchmark/benchmark.py:230
• Issue: for error in result.errors[:3] - hardcoded "3"
• Recommendation: Extract to a named constant like MAX_ERRORS_TO_DISPLAY = 3

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🔒 Security Concerns

1. Path Validation

• Location: src/ragent_lab/ocr_benchmark/strategies.py:66, 162
• Issue: No validation that image paths exist or are valid files
• Recommendation: Add basic validation to prevent errors and improve error messages

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🚀 Performance Considerations

1. Lazy Loading ✅

• The _get_ocr() pattern for lazy initialization is excellent and saves memory

2. Batch Processing Not Implemented

• supports_batch=False for both PaddleOCR and Tesseract
• Consider implementing optimized batch processing for PaddleOCR in future iterations

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📊 Test Coverage

Good Coverage ✅

• Base classes
• Registry functionality
• Benchmark initialization
• Text similarity calculation

Missing Tests ❌

• No integration tests with actual images
• Error path testing (missing files, corrupted images)
• Ground truth accuracy calculation
• compare_models() method

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📝 Documentation

Excellent Documentation Overall! The docs/ocr_benchmark.md is comprehensive.

Minor Suggestions

1. System Dependencies

• Add system requirements for Tesseract to the main README
• Mention that Tesseract engine needs to be installed system-wide

2. DeepSeek OCR

• Currently registered but not implemented
• Consider removing from default registry or adding "COMING SOON" markers

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🎯 Recommendations Summary

Must Fix (Before Merge)

1. Fix resource leak with Image.open() - use context manager
2. Move difflib import to top of file
3. Remove unused time import from base.py

Should Fix (High Priority)

1. Add file existence validation before OCR operations
2. Replace print() with logging module
3. Add error path testing

Nice to Have

1. Extract magic numbers to constants
2. Add integration tests with test images
3. Implement batch processing optimization

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✨ Final Verdict

Recommendation: Approve with Minor Changes

This is a well-architected feature addition that follows the existing codebase patterns. The code quality is high, documentation is excellent, and the design is extensible. The issues identified are mostly minor.

What I Love:

• Clean architecture with proper separation of concerns
• Excellent documentation
• Good use of design patterns (Strategy, Registry)
• Type hints throughout
• Lazy loading for dependencies

Priority Fixes:

1. Fix the Image resource leak
2. Add file existence validation
3. Move import to module level

Great work! 🎉

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Code Review by Claude