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Handwritten doctor’s notes must be interpreted accurately and quickly in the rapidly changing healthcare industry to support administrative duties, medical research, and patient care. Doctors sometimes use handwritten notes, which can differ greatly in style and legibility, to record their observations, diagnoses, and treatment recommendations. Since providing the best possible patient care depends on the precise interpretation of these notes, this unpredictability presents a serious problem to healthcare practitioners. In this paper, the CNNs method has been proposed to identify doctors’ notes. Overall, this paper aims to improve the efficiency and accuracy of interpreting handwritten doctors’ notes by leveraging handwriting recognition tμechniques and CNNs. The accuracy of the proposed method is 81%. By automating this process, healthcare professionals can save time, reduce errors, and improve patient care by quickly accessing and analyzing the relevant information within medical documents.

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Interpreting Doctor’s Notes Using Handwriting Recognition and Convolutional Neural Networks (CNNs)

  • 会议论文

  • 发表日期：

    2025年

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  • Abhishek Choubey,
  • Koushik Margam,
  • Vikas Reddy Sheelam,
  • Shiva Ananthula,
  • Shruti Bhargava Choubey

摘要

Handwritten doctor’s notes must be interpreted accurately and quickly in the rapidly changing healthcare industry to support administrative duties, medical research, and patient care. Doctors sometimes use handwritten notes, which can differ greatly in style and legibility, to record their observations, diagnoses, and treatment recommendations. Since providing the best possible patient care depends on the precise interpretation of these notes, this unpredictability presents a serious problem to healthcare practitioners. In this paper, the CNNs method has been proposed to identify doctors’ notes. Overall, this paper aims to improve the efficiency and accuracy of interpreting handwritten doctors’ notes by leveraging handwriting recognition tμechniques and CNNs. The accuracy of the proposed method is 81%. By automating this process, healthcare professionals can save time, reduce errors, and improve patient care by quickly accessing and analyzing the relevant information within medical documents.