Interpreting Doctors' Notes Using Handwriting Recognition and Deep Learning Techniques

This research provides a deep learning approach and handwriting recognition to understand medical notes. The system processes and interprets textual medical notes using connectionist temporal classification (CTC) and optical character recognition (OCR) techniques. The written text is transcribed usi...

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Bibliographic Details
Published in2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) pp. 1 - 7
Main Authors G, Maheswari, R, Ramya, S, Babitha, R, Sriram, M, Jennis, P, I RamPrasad
Format Conference Proceeding
LanguageEnglish
Published IEEE 04.04.2024
Subjects
Accuracy
Connectionist Temporal Classification
Deep learning
Doctors' Notes
Handwriting recognition
Interpreting
Optical character recognition
Optical recording
Software algorithms
Training
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Summary:This research provides a deep learning approach and handwriting recognition to understand medical notes. The system processes and interprets textual medical notes using connectionist temporal classification (CTC) and optical character recognition (OCR) techniques. The written text is transcribed using the OCR component, and then the CTC algorithm is utilized to analyze the text and give significance to particular note portions. The suggested system's ability to correctly understand and arrange written medical information is demonstrated by training it on prescription notes from doctors taken from a dataset. According to the final forecast, the system can read medical notes with high accuracy, making medical professionals' usage and accessibility of these records easier. The suggested paradigm offers fast and precise access to patient information, which could increase the efficacy and efficiency of medical care.
DOI:10.1109/ICONSTEM60960.2024.10568799