Home⇛Isabela State University Linker: Journal of Education, Social Sciences and Allied Health⇛vol. 2 no. 1 (2025)

Real-Time CNN-Based Generic Medicine Name Classification for Prescription Recognition

Era B. Espiras | Leika Anne P. Galvez | Ma. Yalaine S. Merino | Crismar C. Ramos | Ronan D. Soriano

Discipline: Pharmacy

 

Abstract:

Illegible handwriting in prescriptions remains a prevalent issue in fast-paced healthcare environments, contributing to misinterpretation and medication errors. Recognizing the importance of prescription readability, this study developed and deployed a real-time web application that classifies 20 generic medicine names using a Convolutional Neural Networks (CNN) model, making the system more accessible for real-world use. A dataset of 2,100 images was collected from public and private hospitals in Quezon City, and expanded to 6,720 images using data augmentation techniques such as brightening, blurring, and noise reduction. Some medicines, such as Chlorpromazine and Hydroxyzine, showed slightly lower performance, suggesting the need for more diverse data. The results demonstrate the model’s reliability and potential for integration into hospital systems or pharmacy management software, offering a practical solution to reduce errors in medication dispensing. Future work could involve expanding the dataset and integrating the model with OCR or electronic health record (EHR) systems to support broader handwriting variations and real-time clinical workflows.



References:

  1. Bhalla, A., Garg, S., & Singh, P. (2020). Present day web-development using ReactJS. International Research Journal of Engineering and Technology (IRJET), 7(5), 4410–4413.
  2. Cerio, A. A. P., Mallare, N. A. L. B., & Tolentino, R. M. S. (2015). Assessment of the legibility of the handwriting in medical prescriptions of doctors from public and private hospitals in Quezon City, Philippines. Procedia Manufacturing, 3, 90–97. https://doi.org/10.1016/j.promfg.2015.07.112
  3. Fajardo, L. J., Sorillo, N. J., Garlit, J., Tomines, C. D., Abisado, M. B., Imperial, J. M. R., Rodriguez, R. L., & Fabito, B. S. (2019, November 1). Doctor’s cursive handwriting recognition system using deep learning. In 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) (pp. 1-6). IEEE. https://doi.org/10.1109/hnicem48295.2019.9073521
  4. Jain, T., Sharma, R., & Malhotra, R. (2021). Handwriting recognition for medical prescriptions using a CNN-Bi-LSTM model. In 2021 6th International Conference on Inventive Computation Technologies (ICICT) (pp. 824-829). IEEE. https://doi.org/10.1109/i2ct51068.2021.9418153
  5. Kamble, N. C. B., & Mane, N. K. T. (2024). A review on handwritten recognition system using machine learning techniques. International Research Journal on Advanced Engineering Hub (IRJAEH), 2(6), 1590–1599. https://doi.org/10.47392/irjaeh.2024.0218
  6. Memon, J., Sami, M., Khan, R. A., & Uddin, M. (2020). Handwritten optical character recognition (OCR): A comprehensive systematic literature review (SLR). IEEE Access, 8, 142642–142668. https://doi.org/10.1109/access.2020.3012542
  7. OjaswiTech. (n.d.). Web applications, their use, benefits and importance of web applications today. OjaswiTech. Retrieved April 12, 2025, from https://www.ojaswitech.com/web-design-technologies/web-applications
  8. Parashar, A. (2017). Importance of computer vision for human life. International Journal of Advanced Research, 5(3), 2396–2399. https://doi.org/10.21474/ijar01/3769
  9. Prasantha, S. (2023). Detailed survey of handwriting recognition using machine learning algorithms. International Journal of Creative Research Thoughts, 11(1), B704–B708. https://www.researchgate.net/publication/377626296_Detailed_Survey_Of_Handwriting_Recognition_Using_Machine_Learning_Algorithms
  10. Ratanto, N., Hariyati, R. T. S., Mediawati, A. S., & Eryando, T. (2021). Workload as the most important influencing factor of medication errors by nurses. The Open Nursing Journal, 15(1), 204–210. https://doi.org/10.2174/1874434602115010204
  11. Shehab, M., Abualigah, L., Shambour, Q., Abu-Hashem, M. A., Shambour, M. K. Y., Alsalibi, A. I., & Gandomi, A. H. (2022). Machine learning in medical applications: A review of state-of-the-art methods. Computers in Biology and Medicine, 145, Article 105458. https://doi.org/10.1016/j.compbiomed.2022.105458
  12. Shrawankar, U. (2019). Standardization of handwritten words to improve readability. International Journal of Technology Diffusion, 10(3), 1–17. https://doi.org/10.4018/ijtd.2019070101
  13. Smilkov, D., Thorat, N., Assogba, Y., Yuan, A., Kreeger, N., Yu, P., Zhang, K., Cai, S., Nielsen, E., Soergel, D., Bileschi, S., Terry, M., Nicholson, C., Gupta, S. N., Sirajuddin, S., Sculley, D., Monga, R., Corrado, G., Viégas, F. B., & Wattenberg, M. (2019, January 16). TensorFlow.js: Machine learning for the web and beyond. arXiv. https://arxiv.org/abs/1901.05350
  14. Sotnik, S., Shakurova, T., & Lyashenko, V. (2023). Development features of web-applications. International Journal of Academic and Applied Research (IJAAR), 7(1), 79–85. https://openarchive.nure.ua/entities/publication/0e1b6221-a2c0-4f68-b550-53714391c127
  15. Tabassum, S., Abedin, N., Rahman, M. M., Rahman, M. M., Ahmed, M. T., Islam, R., & Ahmed, A. (2022). An online cursive handwritten medical words recognition system for busy doctors in developing countries for ensuring efficient healthcare service delivery. Scientific Reports, 12(1), 3840. https://doi.org/10.1038/s41598-022-07571-z
  16. Taye, M. M. (2023). Theoretical understanding of convolutional neural network: Concepts, architectures, applications, future directions. Computation, 11(3), 52. https://doi.org/10.3390/computation11030052
  17. Thokala, V. S. (2021). Integrating machine learning into web applications for personalized content delivery using Python. International Journal of Current Engineering and Technology, 11(6), 1083–1087.

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