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Codebase & Experiments

Paper: CIKM Short Paper - Knowing When to Correct: Cost-Aware LLM Routing
Authors: Stergios Konstantinidis, Hayman Lotfy, Michalis Vlachos (University of Lausanne)
Venue: ACM International Conference on Information and Knowledge Management (CIKM) - Short Paper
GitHub: https://github.com/Stergios-Konstantinidis/CIKM_public
Overleaf: https://git.overleaf.com/6a1d78e803cbdf7def32a839

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.
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