A hybrid deep learning-based OCR model for handwritten medical prescriptions.
4 affiliations
- 1College of Pharmacy, Alfaisal University, Riyadh, Saudi Arabia.
- 2Department of Biomedical Engineering, College of Engineering, Al-Nahrain University, Baghdad, Iraq.
- 3Technical College of Engineering, Al-Bayan University, Baghdad, Iraq.
- 4Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
Abstract
The current trend involving the use of paper-based medical prescriptions and the exchange of such by the social media is a major problem in automated and accurate text extraction in healthcare processes. This paper suggests a complete smart pipeline to digitize the images of medical prescriptions with lightweight deep learning-based classification, specialized Optical Character Recognition (OCR) engines, and comparative benchmarking of results using a large language model (LLM). The web scraped images of medical prescriptions are initially processed through a GhostNetV2 classifier to differentiate between handwritten and digital medical prescriptions. According to this choice, digital images are sent to PaddleOCR, and handwritten images are considered in Tesseract OCR so that each image could be processed by the best suitable recognition engine. The OCR outputs from both branches are benchmarked against GPT-4o (OpenAI) as an independent baseline to assess competitive performance and provide error-rate context. Experimental analysis shows that the suggested adaptive routing strategy is characterized by the fact that it has a high classification rate and the character and word error rates are significantly lower than those single-OCR techniques. The system is competitive with, though does not exceed, the raw accuracy of a state-of-the-art LLM-based OCR baseline (GPT-4o), while offering practical advantages in cost, transparency, and on-premises deployability that the black-box, per-call LLM baseline does not. The findings verify that the combination of lightweight classifiers and multi-OCR decision routing and multi-LLM-aided comparative benchmarking offers a scalable and robust solution to real-world medical prescriptions digitization, which adds to better automation, accuracy, and safety in healthcare information processing.
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BibTeX
@article{ameer2026hybrid,
title = {{A hybrid deep learning-based OCR model for handwritten medical prescriptions.}},
author = {Omar Z Ameer and Mahmood Hameed Majeed and Safa L Kailan and Saif Mohamed Baraa Alsabti and Ali M Elhenidy},
journal = {Frontiers in medicine},
year = {2026},
month = oct,
doi = {10.3389/fmed.2026.1856485},
url = {https://europepmc.org/article/MED/42898190},
note = {CC BY},
}
RIS
TY - JOUR
TI - A hybrid deep learning-based OCR model for handwritten medical prescriptions.
AU - Omar Z Ameer
AU - Mahmood Hameed Majeed
AU - Safa L Kailan
AU - Saif Mohamed Baraa Alsabti
AU - Ali M Elhenidy
PY - 2026
DA - 2026/10/05
JO - Frontiers in medicine
DO - 10.3389/fmed.2026.1856485
UR - https://europepmc.org/article/MED/42898190
AB - The current trend involving the use of paper-based medical prescriptions and the exchange of such by the social media is a major problem in automated and accurate text extraction in healthcare processes. This paper suggests a complete smart pipeline to digitize the images of medical prescriptions with lightweight deep learning-based classification, specialized Optical Character Recognition (OCR) engines, and comparative benchmarking of results using a large language model (LLM). The web scraped images of medical prescriptions are initially processed through a GhostNetV2 classifier to differentiate between handwritten and digital medical prescriptions. According to this choice, digital images are sent to PaddleOCR, and handwritten images are considered in Tesseract OCR so that each image could be processed by the best suitable recognition engine. The OCR outputs from both branches are benchmarked against GPT-4o (OpenAI) as an independent baseline to assess competitive performance and provide error-rate context. Experimental analysis shows that the suggested adaptive routing strategy is characterized by the fact that it has a high classification rate and the character and word error rates are significantly lower than those single-OCR techniques. The system is competitive with, though does not exceed, the raw accuracy of a state-of-the-art LLM-based OCR baseline (GPT-4o), while offering practical advantages in cost, transparency, and on-premises deployability that the black-box, per-call LLM baseline does not. The findings verify that the combination of lightweight classifiers and multi-OCR decision routing and multi-LLM-aided comparative benchmarking offers a scalable and robust solution to real-world medical prescriptions digitization, which adds to better automation, accuracy, and safety in healthcare information processing.
SN - 2296-858X
ER -