# Codebase & Experiments

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# Codebase Guide: Optimizing-LLM-based-OCR-Corrections

## 1. Directory Structure

- `code/experiments/`:
- `confbert_router.py`: ConfBERT token-level scoring and routing pipeline.
- `experiment_linear_regression.py`: Lasso and Ridge feature selection and cross-validation.
- `experiment_svm_classifier.py`: Support Vector Machine routing.
- `experiment_nn_regression.py`: Neural network router.
- `lazy_clf_scan.py`: AutoML screening of 30+ regression algorithms.
- `code/plotting/`:
- `plot_routing_frontier_paddle.py`: Generates CER vs Cost Pareto curves.
- `plot_threshold_sweep.py`: Threshold sensitivity analysis.
- `plot_error_confidence_cer.py`: Correlation between OCR confidence and CER.
- `code/evaluation/`:
- `run_evaluations.py`: Batch evaluation harness across Tesseract, EasyOCR, and PaddleOCR.
- `data/`:
- `evaluation_dataset/groundtruth.json`: Ground truth transcriptions for 609 segments.
- `raw_ocr_results.json`: Output dumps from Tesseract, EasyOCR, and PaddleOCR.