An Evaluation of Open-Source OCR Models for Japanese Medical Documents
summary
Open-source OCR models are evaluated head-to-head on Japanese medical PDFs using accuracy, memory use, and speed; Tesseract PaddleOCR and EasyOCR are tested on three clinical report types, main-body text hits ~96%+ accuracy while sparse or image-overlaid labels fail, doubling image resolution cuts errors (Tesseract mistakes 15 to 3), EasyOCR runs 1,000 docs in ~177 min CPU or ~52 min with GPU acceleration, Tesseract is fastest on CPU, PaddleOCR faces CUDA/documentation friction and setup issues, Google Vertex shows near 100% accuracy.
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chapters (19)
- 0:00Introduction
- 1:05Evaluating OCR for Japanese medical docs
- 6:32Leading open-source OCR models
- 8:45OCR image-to-text process
- 9:30OCR models CRNN architecture
- 12:24Model architecture differences
- 14:36Medical documents for evaluation
- 16:44OCR accuracy metrics
- 18:06Results on first medical document
- 19:34Image resolution impact on OCR
- 20:17Results on second medical document
- 21:50Results on third medical document
- 24:05Accuracy and segmentation summary
- 25:08Time efficiency comparison
- 27:37Key takeaways and recommendations
- 28:46Q&A on model performance
- 31:39Q&A on career path
- 34:29Q&A on cost alternatives
- 35:54Closing remarks
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