Codebase & Experiments

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.