Benchmarking four open-source OCR engines on 5,578 handwritten medical prescriptions
Key Takeaways
- PP-OCRv5 (5M parameters) and GLM-OCR (0.9B parameters) both achieve 20%+ exact match on handwritten prescriptions, a 10x jump over Tesseract and EasyOCR
- GLM-OCR leads on character accuracy (CER 0.328), while PP-OCRv5 leads on word accuracy (WER 0.789)
- A 5M-parameter model trained on curated data rivals a 900M-parameter vision-language model
- Neither engine is clinically deployable yet: even the best gets only 1 in 3 words exactly right
Last month I spent some time squinting at prescription scans, trying to figure out if a doctor wrote Amoxicillin or Amitriptyline. I got it wrong twice. That got me wondering: how would today's OCR engines handle this?
The stakes are real. Medication errors injure approximately 1.3 million people annually in the United States alone and cost an estimated $42 billion globally (WHO, 2017). Illegible handwriting is a well-documented contributor: 35.7% of handwritten prescriptions contain errors, compared to just 2.5% of electronic ones (Albarrak et al., 2014).
A study of 4,183 prescriptions found that 10.21% of them are illegible, and 19.39% are barely legible (Albalushi et al., 2023). The global OCR market is projected to reach %%MATH_BLOCK0%% \sqrt{e} x{0} $` (CER=4.75). This is a rare but real failure mode of generative VLMs: the model interprets the handwriting as a math expression instead of text. It produced more characters than the ground truth has, which is why CER exceeds 1.0.
What Are the Limitations?
- Single dataset, single run. RxHandBD is one dataset of Bangladeshi prescriptions. US, European, or East Asian handwriting styles may produce different rankings. We don't have confidence intervals from multiple runs.
- Word-level only. We tested recognition on pre-cropped word images, not full-page detection + recognition. Real-world performance depends on the complete pipeline.
- CPU-only. All engines ran on CPU. GPU acceleration could significantly change the latency picture, particularly for GLM-OCR.
- Default configs. No engine was fine-tuned on medical data. Domain-specific adaptation could improve all of them.
- No clinical validation. OCR accuracy and clinical safety are different things. A 32.6% exact match rate is impressive for research, but not nearly sufficient for automated prescription processing without human review.
Conclusion
Modern OCR has made a genuine leap on handwritten medical text. PP-OCRv5 (5M parameters, best word-level accuracy) and GLM-OCR (0.9B parameters, best character-level accuracy) both dramatically outperform Tesseract and EasyOCR.
The two champions represent fundamentally different design philosophies: a data-centric specialized pipeline vs. a compact vision-language model. Yet they arrive at remarkably similar performance levels. Both are open-source and practically deployable.
For practitioners building healthcare OCR systems: these two engines deserve serious evaluation. Start with your specific error tolerance, hardware constraints, and whether you need word-level recognition or full-page document understanding.
For researchers: this is a domain with high clinical impact and, as these results show, plenty of room for improvement. Even the best engine here gets only 1 in 3 words exactly right on doctor handwriting. There's real work left to do.
What datasets or engines should I test next? Let me know in the comments.
References
World Health Organization. "WHO launches global effort to halve medication-related errors in 5 years." March 29, 2017. https://www.who.int/news/item/29-03-2017-who-launches-global-effort-to-halve-medication-related-errors-in-5-years
Albarrak AI, Al Rashidi EA, Fatani RK, Al Ageel SI, Mohammed R. "Assessment of legibility and completeness of handwritten and electronic prescriptions." Saudi Pharmaceutical Journal, 2014. https://pmc.ncbi.nlm.nih.gov/articles/PMC4281619/
Albalushi AK, et al. "Assessment of Legibility of Handwritten Prescriptions and Adherence to W.H.O. Prescription Writing Guidelines." J. of Pharmaceutical Research International, 2023. https://pmc.ncbi.nlm.nih.gov/articles/PMC10686667/
Cui C, Zhang Y, Sun T, et al. "PP-OCRv5: A Specialized 5M-Parameter Model Rivaling Billion-Parameter Vision-Language Models on OCR Tasks." arXiv:2603.24373, March 2026. https://arxiv.org/abs/2603.24373
Duan S, Xue Y, Wang W, et al. "GLM-OCR Technical Report." arXiv:2603.10910, March 2026. https://arxiv.org/abs/2603.10910
Shovon MSH, et al. "RxHandBD: A Handwritten Prescription Recognition Dataset from Bangladesh." Mendeley Data. https://data.mendeley.com/datasets
Grand View Research. "Optical Character Recognition Market Analysis." 2025. https://www.grandviewresearch.com/industry-analysis/optical-character-recognition-market
Benchmark conducted independently. No affiliation with any OCR project. All engines evaluated using default configurations.
About the author: Botao Deng is an ML/AI engineer and researcher who builds and evaluates production models.GitHub
