ARCHIVES

1. Pranit Patil, Bhupinder Kaur, “Handwritten Digit Recognition Using Various Machine Learning Algorithms and Models”, International
Journal of Innovative Research in Computer Science & Technology (IJIRCST), Volume-8, Issue-4, July-2020.
2. Rohini. M, Dr. D. Surendran, “A NOVEL METHOD FOR HAND WRITTEN DIGIT RECOGNITION USING DEEP LEARNING”,
INTERNATIONAL JOURNAL OF CURRENT ENGINEERING AND SCIENTIFIC RESEARCH (IJCESR), Volume-6, Issue-6, 2019.
3. Ayesha Siddiqa, Chakrapani. D. S, “A Recognition System for Handwritten Digits Using CNN”, International Journal of Science and
Research (IJSR), Volume-10, Issue-10, October-2021.
4. Ritik Dixit, Rishika Kushwah, Samay Pashine, “Handwritten Digit Recognition using Machine and Deep Learning Algorithms”,
arXiv:2106.12614v1 [cs.CV] 23 June 2021.
5. Wang, Y., Wang, R., Li, D. et al. Improved Handwritten Digit Recognition using Quantum K-Nearest Neighbor Algorithm. Int J Theor
Phys 58, 2331–2340 (2019).
6. M. B. Abdulrazzaq and J. N. Saeed, "A Comparison of Three Classification Algorithms for Handwritten Digit Recognition," 2019
International Conference on Advanced Science and Engineering (ICOASE), Zakho - Duhok, Iraq, 2019, pp. 58-63, doi:
10.1109/ICOASE.2019.8723702.
7. Assegie, Tsehay & Nair, Pramod. (2019), "Handwritten digits recognition with decision tree classification: a machine learning
approach", International Journalof Electrical and Computer Engineering (IJECE). 9. 4446. 10.11591/ijece.v9i5.pp4446-4451. K. Elissa,
“Title of paper if known,” unpublished.
8. D. Ge, X. Yao, W. Xiang, X. Wen and E. Liu, "Design of High Accuracy Detector for MNIST Handwritten Digit Recognition Based on
Convolutional NeuralNetwork," 2019 12th International Conference on Intelligent Computation Technology and Automation (ICICTA),
Xiangtan, China, 2019, pp. 658-662, doi: 10.1109/ICICTA49267.2019.00145.
9. Al-Wzwazy, Haider. (2016), "Handwritten Digit Recognition Using Convolutional Neural Networks", International Journal of Innovative
Research in Computer and Communication Engineering. 4.
10. Hafiz, Abdul & Bhat, Ghulam. (2020), "Reinforcement Learning Based Handwritten Digit Recognition with Two-State Q-Learning",
2007.01193.
11. Khan, H. (2017) MCS HOG Features and SVM BasedHandwritten Digit Recognition System. Journal of Intelligent Learning Systems
and Applications, 9, 21-33.
12. M. Y. W. Teow, "Understanding convolutional neural networks using a minimal model for handwritten digit recognition," 2017
IEEE 2nd International Conference on Automatic Control and Intelligent Systems (I2CACIS), Kota Kinabalu, 2017, pp. 167-172, doi:
10.1109/I2CACIS.2017.8239052.
13. Alsaafin, A. and Elnagar, A. (2017) A Minimal Subsetof Features Using Feature Selection for Handwritten Digit Recognition. Journal of
Intelligent Learning Systems and Applications, 9, 55-68.
14. Wu S., Wei W., Zhang L. (2018) Comparison of Machine Learning Algorithms for Handwritten Digit Recognition. In: Li K., Li W.,
Chen Z., Liu Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information
Science, vol 874. Springer, Singapore.
15. R. Jantayev and Y. Amirgaliyev, "Improved Handwritten Digit Recognition method using Deep Learning Algorithm," 2019 15th
International Conference on Electronics, Computer and Computation (ICECCO), Abuja, Nigeria, 2019, pp. 1-4, doi:
10.1109/ICECCO48375.2019.9043235.