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This audit tests EasyOCR as a replacement for Tesseract on tables, invoices, driver's licenses and handwriting. The presenter reports better accuracy and GPU speeds close to Tesseract, but assigns EasyOCR a C-tier rating after difficult documents expose its limits. These are results from the speaker's test set, rather than a universal ranking.
The setup uses Python 3.11 and CUDA 12.8, with Pillow 10.0 pinned to avoid installation errors. The presenter describes EasyOCR as an Apache 2.0 open source model with local execution and zero API exposure. The tutorial initializes the Reader once in VRAM and measures processing with a timer decorator. It also explains how a mismatched GPU environment causes CPU fallback.
High-contrast table text fares well, though the output is a list of strings rather than structured JSON. The invoice loses spatial relationships between prices and line items. Cleanup examples remove recurring punctuation artifacts, while a dictionary of state names supports fuzzy matching for license errors.
Handwriting produces largely unusable text in this audit. The final unstructured form also performs poorly, with most confidence scores below 0.5. The speaker cautions that threshold changes, detection margins and image preprocessing can help one document while hurting another. The supplied GitHub repository contains the audit scripts and environment details for testing other datasets.
