Handwritten Digit Recognition Using Various Machine Learning Algorithms and Models
Keywords:
Convolutional Neural Network, Support Vector Machine, HandWritten Digit Recognition, Artificial Intelligence, Deep LearningAbstract
Handwritten digit recognition is a technique or technology for automatically recognizing and detecting handwritten digital data through different Machine Learning models. In this paper we use various Machine Learning algorithms to enhance the productiveness of technique and reduce the complexity using various models. Machine Learning is an application of Artificial Intelligence that learns from previous experience and improves automatically through experience. We illustrate various Machine learning algorithms such as Support Vector Machine, Convolutional Neural Network, Quantum Computing, K-Nearest Neighbor Algorithm, Deep Learning used in Recognition technique.
Downloads
References
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).
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.
Assegie, Tsehay & Nair, Pramod. (2019). Handwritten digits recognition with decision tree classification: a machine learning approach. International Journal of Electrical and Computer Engineering (IJECE). 9. 4446. 10.11591/ijece.v9i5.pp4446-4451. K. Elissa, “Title of paper if known,” unpublished.
D. Ge, X. Yao, W. Xiang, X. Wen and E. Liu, "Design of High Accuracy Detector for MNIST Handwritten Digit Recognition Based on Convolutional Neural Network," 2019 12th International Conference on Intelligent Computation Technology and Automation (ICICTA), Xiangtan, China, 2019, pp. 658-662, doi: 10.1109/ICICTA49267.2019.00145.
Al-Wzwazy, Haider. (2016). Handwritten Digit Recognition Using Convolutional Neural Networks. International Journal of Innovative Research in Computer and Communication Engineering. 4.
Hafiz, Abdul & Bhat, Ghulam. (2020). Reinforcement Learning Based Handwritten Digit Recognition with Two-State Q-Learning. 2007.01193.
Khan, H. (2017) MCS HOG Features and SVM Based Handwritten Digit Recognition System. Journal of Intelligent Learning Systems and Applications, 9, 21-33.
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.
Alsaafin, A. and Elnagar, A. (2017) A Minimal Subset of Features Using Feature Selection for Handwritten Digit Recognition. Journal of Intelligent Learning Systems and Applications, 9, 55-68.
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
Jha, G., Cecotti, H. Data augmentation for handwritten digit recognition using generative adversarial networks. Multimed Tools Appl (2020).
Kulkarni, S. R., & Rajendran, B. (2018). Spiking neural networks for handwritten digit recognition—Supervised learning and network optimization. Neural Networks, 103, 118–127.
Qiao, J., Wang, G., Li, W., & Chen, M. (2018). An adaptive deep Q-learning strategy for handwritten digit recognition. Neural Networks.
S. Aly and S. Almotairi, "Deep Convolutional Self-Organizing Map Network for Robust Handwritten Digit Recognition," in IEEE Access, vol. 8, pp. 107035-107045, 2020, doi: 10.1109/ACCESS.2020.3000829.
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.
