How we made our optical character recognition (OCR) code more accurate

a year ago

OCR technology converts printed or handwritten characters from images into machine-readable text.
Pieces enhanced Tesseract OCR for code by adding pre- and post-processing steps.
Pre-processing includes handling dark-mode images, noisy backgrounds, and low-resolution images.
Post-processing involves inferring code indentation using bounding boxes from Tesseract.
Evaluation uses datasets and Levenshtein distance to compare predicted text with ground truth.
Bicubic upsampling was chosen over super-resolution models for better efficiency.
Pieces provides a fine-tuned OCR model for code, available in their desktop app.

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