Journal of Digital Information Management


Vol No. 21 ,Issue No. 4 2024

Convolutional Neural Networks for Handwritten Text Recognition of Medical Prescription
Makarand Shahade, Mayuri Kulkarni, Vivek Pawar, Jatin Chaudhari, Yash Lakade, Darshan Kotka
SVKM’s Institute of Technology Dhule, India
Abstract: Converting handwritten prescriptions into electronic format offers several advantages and is crucial for modern healthcare systems. It is essential nowadays because of some factors such as – Legibility and Accuracy: Handwritten prescriptions can be challenging to read and interpret; accessibility and Portability: Electronic prescriptions can be easily stored; Decision Support Systems: By digitising prescriptions, healthcare systems can integrate them with electronic health records (EHRs) and utilise decision support systems. Convolutional neural networks (CNNs) are a class of deep learning algorithms that have proven effective in extracting handwritten text from various documents, including medical prescriptions. By leveraging CNNs for handwritten text extraction, healthcare systems can automate the process of digitising prescriptions, reducing manual effort and potential human errors. This enables seamless integration with electronic systems, facilitating better patient care and overall healthcare management. In this paper, we have trained the CNN model for different parameters and observed the accuracy and loss for various parameters. We got a maximum training accuracy of 89% and a maximum testing accuracy of 70%.
Keywords: Handwritten Characters, Text recognition, Neural Networks, Electronic Health Records, Medical Prescription Convolutional Neural Networks for Handwritten Text Recognition of Medical Prescription
DOI:https://doi.org/10.6025/jdim/2023/21/4/117-124
Full_Text   PDF 1.14 MB   Download:   48  times
References:

[1] Cowie, M. R., Blomster, J. I., Curtis, L. H., Duclaux, S., Ford, I., Fritz, F., others. (2017). Electronic health records to facilitate clinical research. Clinical Research in Cardiology, 106(1), 1–9.
[2] Häyrinen, K., Saranto, K., Nykänen, P. (2008). Definition, structure, content, use and impacts of electronic health records: a review of the research literature. International Journal of Medical Informatics, 77, 291–304.
[3] Waegemann, C. P., Tessier, C., Barbash, A., Blumenfeld, B. H., Borden, J., Brinson Jr, R. M. (2002). Healthcare documentation: A report on information capture and report generation. Newton, MA: Medical Records Institute.
[4] LeCun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature, 521, 436–444.
[5] Mithe, R., Indalkar, S., Divekar, N. (2013). Optical character recognition. International Journal of Recent Technology and Engineering (IJRTE), 2, 72–75.
[6] Dhar, D., Garain, A., Singh, P. K., Sarkar, R. (2021). HP_DocPres: A method for classifying printed and handwritten texts in doctor’s prescription. Multimedia Tools and Applications, 80, 9779–9812.
[7] Jain, T., Sharma, R., Malhotra, R. (2021). Handwriting recognition for medical prescriptions using a CNN-BiLSTM model. In: 2021 6th International Conference for Convergence in Technology (I2CT).
[8] Hassan, E., Tarek, H., Hazem, M., Bahnacy, S., Shaheen, L., Elashmwai, W. H. (2021). Medical prescription recognition using machine learning. In: 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC).
[9] Achkar, R., Ghayad, K., Haidar, R., Saleh, S., & Al Hajj, R. (2019). Medical handwritten prescription recognition using CRNN. In: 2019 International Conference on Computer, Information and Telecommunication Systems (CITS).
[10] Tabassum, S., Abedin, N., 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, 3601.
[11] Balci, B., Saadati, D., Shiferaw, D. (2017). Hand124 Journal of Digital Information Management Volume 21 Number 4  December 2023 written Text Recognition using Deep Learning. Report of Stanford University. Link to the report.
[12] Bhattacharyya, S. (2011). A brief survey of color image preprocessing and segmentation techniques. Journal of Pattern Recognition Research, 1, 120–129.
[13] Pal, K. K., Sudeep, K. S. (2016). Preprocessing for image classification by convolutional neural networks. In: 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT).
[14] Kohli, R., Tan, S. S.-L. (2016). Electronic Health Records. MIS Quarterly, 40, 553–574.
[15] Jha, A. K., DesRoches, C. M., Campbell, E. G., Donelan, K., Rao, S. R., Ferris, T. G., Blumenthal, D. (2009). Use of electronic health records in US hospitals. New England Journal of Medicine, 360, 1628–1638.