Classification of Handwritten Digits on the web using Deep Learning


Rutuj Runwal
Shrawan J Purve
Mohit Chandak


Development of a handwriting classifier using deep learning approach that can classify handwritten numbers and digits on the web. It is a deep-learning based system that uses modern algorithms and focuses on creating a portable web application that aims to classify handwritten numbers powered by the MNIST dataset


How to Cite
Rutuj Runwal, Shrawan J Purve, & Mohit Chandak. (2023). Classification of Handwritten Digits on the web using Deep Learning. International Journal of Next-Generation Computing, 14(1).


  1. Arnold, L., Rebecchi, S., Chevallier, S., & Paugam-Moisy, H. (2011, April). An introduction to deep learning. In European Symposium on Artificial Neural Networks (ESANN).
  2. Eikvil, L. (1993). Optical character recognition. citeseer. ist. psu. edu/142042. html, 26.
  3. Donoser, M., & Bischof, H. (2006, June). Efficient maximally stable extremal region (MSER) tracking.
  4. In 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR'06) (Vol. 1, pp. 553-560). Ieee.
  5. Deng, L. (2012). The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE signal processing magazine, 29(6), 141-142. DOI:
  6. O'Shea, K., & Nash, R. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458.
  7. Smilkov, D., Thorat, N., Assogba, Y., Nicholson, C., Kreeger, N., Yu, P., ... & Wattenberg, M.
  8. M. (2019). Tensorflow. js: Machine learning for the web and beyond. Proceedings of Machine Learning and Systems, 1, 309-321.
  9. Alkhawaldeh, Rami S., et al. "Ensemble deep transfer learning model for Arabic (Indian) handwritten digit recognition." Neural Computing and Applications 34.1 (2022): 705-719. DOI:
  10. Singh, Divya, Shahana Bano, Debarata Samanta, M. S. Mekala, and S. K. Islam. "Deep Learning Inspired Nonlinear Classification Methodology for Handwritten Digits Recognition Using DSR Encoder." Arabian Journal for Science and Engineering (2022): 1-13. DOI:
  11. Haghighi, Fatemeh, and Hesam Omranpour. "Stacking ensemble model of deep learning and its application to Persian/Arabic handwritten digits recognition." Knowledge-Based Systems 220 (2021): 106940. DOI:
  12. Mhasakar, Purva, Prapti Trivedi, Srimanta Mandal, and Suman K. Mitra. "Handwritten Digit Recognition Using Bayesian ResNet." SN Computer Science 2, no. 5 (2021): 1-10. DOI:
  13. Assegie, Tsehay Admassu, and Pramod Sekharan Nair. "Handwritten digits recognition with decision tree classification: a machine learning approach." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 4446-4451. DOI:
  14. Aly, Saleh, and Sultan Almotairi. "Deep convolutional self-organizing map network for robust handwritten digit recognition." IEEE Access 8 (2020): 107035-107045. DOI:
  15. Abdulrazzaq, Maiwan Bahjat, and Jwan Najeeb Saeed. "A comparison of three classification algorithms for handwritten digit recognition." In 2019 International Conference on Advanced Science and Engineering (ICOASE), pp. 58-63. IEEE, 2019. DOI:
  16. Ali, Saqib, Zeeshan Shaukat, Muhammad Azeem, Zareen Sakhawat, and Tariq Mahmood. "An efficient and improved scheme for handwritten digit recognition based on convolutional neural network." SN Applied Sciences 1, no. 9 (2019): 1-9. DOI:
  17. Bouchene, Mohammed Mehdi, and Abdelhak Boukharouba. "Features extraction and reduction techniques with optimized SVM for Persian/Arabic handwritten digits recognition." Iran Journal of Computer Science (2022): 1-19 DOI:

Most read articles by the same author(s)