Artificial intelligence powered OCR models for digitizing ayurveda manuscripts
Journal name: Journal of Ayurveda and Holistic Medicine
Original article title: Artificial intelligence powered OCR models for digitizing ayurveda manuscripts
The Journal of Ayurveda and Holistic Medicine (JAHM) promotes Ayurvedic scientific research by offering a platform for in-depth exploration and integration of its principles into modern healthcare. JAHM is owned and published by Atreya Ayurveda Publications. Atreya Ayurveda Publications engaged in scientific publications on Ayurveda in the form of print books and ebooks since 2007.
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Dr Sourav
2nd year PG scholar, department of shalyatantra, government ayurveda college, Thiruvananthapuram
Dr Resmi
Professor and HOD, Department of Samhitha Samskrita & siddhanta, Government Ayurveda College, Trivandrum
Journal of Ayurveda and Holistic Medicine:
Full text available for: Artificial intelligence powered OCR models for digitizing ayurveda manuscripts
Year: 2024 | Doi: 10.70066/jahm.v12i9.1467
Copyright (license): CC BY-NC-SA 4.0
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Summary of article contents:
This article presents a novel approach to transcribe Ayurveda manuscripts using Optical Character Recognition (OCR) technology enhanced with machine learning and forensic handwriting analysis techniques. Traditional transcription methods for these manuscripts are time-consuming and challenging due to diverse scripts and handwriting styles. The proposed methodology involves collecting manuscript samples from various sources and training OCR models to recognize the nuances of these scripts. By incorporating forensic handwriting principles, such as consistency and natural variation, the OCR models achieve greater accuracy.
Additionally, the development of user-friendly interfaces allows scholars to train these models without needing technical expertise, streamlining the digitization process. Preliminary results show that this approach significantly improves transcription accuracy compared to conventional OCR software. This method offers a scalable solution for digitizing Ayurveda texts, preserving cultural heritage, and accelerating research in Ayurveda medicine. Future research will focus on refining OCR models and expanding their application to other scripts and languages.
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Devanagari script, Ayurveda manuscripts, Machine Learning, User-friendly interface, Technical expertise, Deep Learning, Recurrent Neural Networks, Training Data, Deep Learning Techniques, Convolutional Neural Network, Neural Network.
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