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Article | Green Intelligent Systems and Applications | Volume 4 - Issue 1 - 2024 | 41-53 | https://doi.org/10.53623/gisa.v4i1.417
© 2024 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Transcribing Handwritten Medical Prescription using Convolutional Neural Network AlexNet Architecture and Canny Edge Detection

Author(s): Ralph Andrei A. Benitez 1 , Donata D. Acula 1 , 2 , , Anton Oliver M. Bondoc 1 , Angelo Louis L. Hizon 1 , Aaron Joseph D. Santos 1
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1 Department of Computer Science, College of Information and Computing Sciences, University of Santo Tomas, Manila, Philippines
2 Research Center for Natural and Applied Sciences, University of Santo Tomas, Manila, Philippines

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Green Intelligent Systems and Applications

Volume 4 - Issue 1 - 2024

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Misinterpreted medical prescriptions had led to casualties due to the illegible cursive handwriting of medical practitioners. Many studies focused on this problem. However, the accuracy was unsatisfactory and needed improvement. The study evaluated the performance of the Canny edge detection with other preprocessing methods, including RGB to Grayscale Conversion, Binarization, and Inversion, which was used to process the images of cursive handwritten medical prescriptions using Alexnet Convolutional Recurrent Neural Network (ACoRNN). The CRNN model developed by previous researchers was used as the basis for comparison, and the researchers created a faster and more accurate model. The best combination of preprocessing methods for ACoRNN was with RGB to Grayscale Conversion, Binarization, Canny edge detection, and Inversion. The researchers’ model had faster preprocessing and testing time and achieved 90.76% average accuracy through five trials.

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SUBMITTED: 01 March 2024
ACCEPTED: 28 May 2024
PUBLISHED: 22 June 2024
SUBMITTED to ACCEPTED: 89 days
DOI: https://doi.org/10.53623/gisa.v4i1.417

Cite this article
Benitez, R. A. A., Acula, D. D., Bondoc, A. O. M., Hizon, A. L. L., & Santos, A. J. D. (2024). Transcribing Handwritten Medical Prescription using Convolutional Neural Network AlexNet Architecture and Canny Edge Detection. Green Intelligent Systems and Applications, 4(1), 41–53. https://doi.org/10.53623/gisa.v4i1.417
Keywords
Machine Learning Handwritten Text Recognition Convolutional Neural Network Canny Edge Detection Medical Prescription AlexNet
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