Classification methods for handwritten digit recognition: A survey
Abstract
Introduction/purpose: This paper provides a survey of handwritten digit recognition methods tested on the MNIST dataset.
Methods: The paper analyzes, synthesizes and compares the develop- ment of different classifiers applied to the handwritten digit recognition prob- lem, from linear classifiers to convolutional neural networks.
Results: Handwritten digit recognition classification accuracy tested on the MNIST dataset while using training and testing sets is now higher than 99.5% and the most successful method is a convolutional neural network.
Conclusions: Handwritten digit recognition is a problem with numerous real-life applications. Accurate recognition of various handwriting styles, specifically digits is a task studied for decades and this paper summa- rizes the achieved results. The best results have been achieved with convolutional neural networks while the worst methods are linear classifiers. The con- volutional neural networks give better results if the dataset is expended with data augmentation.
References
Aider, M.A., Hammouche, K. & Gaceb, D. 2018. Recognition of handwritten characters based on wavelet transform and SVM classifier. The International Arab Journal of Information Technology, 15(6), pp. 1082–1087 [online]. Available at: https://iajit.org/portal/PDF/November%202018,%20No.%206/10880.pdf [Accessed: 1 March 2022].
Armstrong, S. 2019. Naive-bayesian-mnist. Github. [online]. Available at: https://github.com/sjnarmstrong/naive-bayesian-mnist [Accessed: 10 March 2022].
Babu, U.R., Chintha, A.K. & Venkateswarlu, Y. 2014. Handwritten digit recognition using structural, statistical features and k-nearest neighbor classifier. International Journal of Information Engineering and Electronic Business (IJIEEB), 6(1), pp. 62–68. Available at: https://doi.org/10.5815/ijieeb.2014.01.07
Bacanin, N., Bezdan, T., Tuba, E., Strumberger, I. & Tuba, M. 2020. Monarch Butterfly Optimization Based Convolutional Neural Network Design. Mathematics, 8(6, art.ID:936), pp. 1–32. Available at: https://doi.org/10.3390/math8060936
Bhagya Shree, S. & Sheshadri, H. 2018. Diagnosis of Alzheimer’s disease using Naive Bayesian Classifier. Neural Computing and Applications, 29, pp. 123– 132. Available at: https://doi.org/10.1007/s00521-016-2416-3
Ciresan, D.C., Meier, U., Gambardella, L.M. & Schmidhuber, J. 2010. Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition. Neural Computation, 22(12), pp. 3207–3220. Available at: https://doi.org/10.1162/NECO_a_00052
Cortes, C. & Vapnik, V. 1995. Support-vector networks. Machine Learning, 20(3), pp. 273–297. Available at: https://doi.org/10.1007/BF00994018
Das, N., Reddy, J.M., Sarkar, R., Basu, S., Kundu, M., Nasipuri, M. & Basu, D.K. 2012. A statistical–topological feature combination for recognition of handwritten numerals. Applied Soft Computing, 12(8), pp. 2486–2495. Available at: https://doi.org/10.1016/j.asoc.2012.03.039
Deotte, C. 2018. How to score 97%, 98%, 99%, and 100%. Kaggle. [online]. Available at: https://www.kaggle.com/c/digit-recognizer/discussion/61480 [Accessed: 10 March 2022].
Ebrahimzadeh, R. & Jampour, M. 2014. Efficient Handwritten Digit Recogni- tion based on Histogram of Oriented Gradients and SVM. International Journal of Computer Applications, 104(9), pp. 10–13. Available at: https://doi.org/10.5120/18229-9167
El qacimy, B., Ait kerroum, M. & Hammouch, A. 2014. Handwritten digit recognition based on DCT features and SVM classifier. In: 2014 Second World Conference on Complex Systems (WCCS). Agadir, Morocco, pp. 13–16, November 10-12. Available at: https://doi.org/10.1109/ICoCS.2014.7060935
Esmaili, I., Dabanloo, N.J. & Vali, M. 2016. Automatic classification of speech dysfluencies in continuous speech based on similarity measures and morphological image processing tools. Biomedical Signal Processing and Control, 23, pp. 104–114. Available at: https://doi.org/10.1016/j.bspc.2015.08.006
Gattal, A., Chibani, Y., Djeddi, C. & Siddiqi, I. 2014. Improving Isolated Digit Recognition Using a Combination of Multiple Features. In: 2014 14th International Conference on Frontiers in Handwriting Recognition. Hersonissos, Greece, pp. 446–451, September 01-04. Available at: https://doi.org/10.1109/ICFHR.2014.81
Grover, D. & Toghi, B. 2020. MNIST dataset classification utilizing k-NN classifier with modified sliding-window metric. In: Arai, K. & Kapoor, S. (Eds.) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing. 944, pp.583–591. Cham, Switzerland: Springer. Available at: par https://doi.org/10.1007/978-3-030-17798-0_47
Huang, H.Y. & Lin, C.J. 2016. Linear and Kernel Classification: When to Use Which? In: Proceedings of the 2016 SIAM International Conference on Data Mining (SDM). Miami, Florida, USA, pp. 216–224, May 5-7. Available at: https://doi.org/10.1137/1.9781611974348.25
Ilmi, N., Budi, W.T.A. & Nur, R.K. 2016. Handwriting digit recognition using local binary pattern variance and K-Nearest Neighbor classification. In: 2016 4th International Conference on Information and Communication Technology (ICoICT). Bandung, Indonesia, pp. 1–5, May 25-27. Available at: https://doi.org/10.1109/ICoICT.2016.7571937
Kang, C., Huo, Y., Xin, L., Tian, B. & Yu, B. 2019. Feature selection and tumor classification for microarray data using relaxed Lasso and generalized multi-class support vector machine. Journal of Theoretical Biology, 463, pp. 77–91. Available at: https://doi.org/10.1016/j.jtbi.2018.12.010
Lecun, Y., Bottou, L., Bengio, Y. & Haffner, P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), pp. 2278–2324. Available at: https://doi.org/10.1109/5.726791
Li, Y., Xiao, J., Chen, Y. & Jiao, L. 2019. Evolving deep convolutional neural networks by quantum behaved particle swarm optimization with binary encoding for image classification. Neurocomputing, 362, pp. 156–165. Available at: https://doi.org/10.1016/j.neucom.2019.07.026
Lyu, H.K., Park, C.H., Han, D.H., Kwak, S.W. & Choi, B. 2018. Orchard Free Space and Center Line Estimation Using Naive Bayesian Classifier for Unmanned Ground Self-Driving Vehicle. Symmetry, 10(9, art.ID:355), pp. 1–14. Available at: https://doi.org/10.3390/sym10090355
Meier, U., Ciresan, D.C., Gambardella, L.M. & Schmidhuber, J. 2011. Better Digit Recognition with a Committee of Simple Neural Nets. In: 2011 International Conference on Document Analysis and Recognition. Beijing, China, pp.1250– 1254, September 18-21. Available at: https://doi.org/10.1109/ICDAR.2011.252
Nosseir, A. & Roshdy, R. 2018. Extraction of Egyptian License Plate Numbers and Characters Using SURF and Cross Correlation. In: ICSIE ’18: Proceedings of the 7th International Conference on Software and Information Engineering. Cairo, Egypt, pp.48–55, May 02-04. Available at: https://doi.org/10.1145/3220267.3220276
Patel, A. & Kalyani, T. 2016. Support Vector Machine with Inverse Fringe as Feature for MNIST Dataset. In: 2016 IEEE 6th International Conference on Advanced Computing (IACC). Bhimavaram, India, pp.123–126, February 27-28. Available at: https://doi.org/10.1109/IACC.2016.32
Sethy, P.K., Barpanda, N.K., Rath, A.K. & Behera, S.K. 2020. Deep feature based rice leaf disease identification using support vector machine. Computers and Electronics in Agriculture, 175, art.number:105527. Available at: https://doi.org/10.1016/j.compag.2020.105527
Sivaram, M., Laxmi, L.E., Pustokhina, I.V., Pustokhin, D.A., Elhoseny, M., Joshi, G.P. & Shankar, K. 2020. An optimal least square support vector machine based earnings prediction of blockchain financial products. IEEE Access, 8, pp. 120321–120330. Available at: https://doi.org/10.1109/ACCESS.2020.3005808
Tuba, E., Bačanin, N., Strumberger, I. & Tuba, M. 2021. Convolutional Neural Networks Hyperparameters Tuning. In: Pap, E. (Eds.) Artificial Intelligence: Theory and Applications. Studies in Computational Intelligence. 973, pp.65–84. Cham, Switzerland: Springer. Available at: par https://doi.org/10.1007/978-3-030-72711-6_4
Tuba, E. & Tuba, I. 2021. Swarm Intelligence Algorithms for Convolutional Neural Networks. In: 2nd Workshop on Evolutionary and Population-based Optimization. Online Event, pp.1–6, November 30. Available at: https://wepo2021.aisylab.com/papers/wepo2021_paper_4.pdf [Accessed: 10 March 2022].
Tuba, E., Tuba, M. & Simian, D. 2016. Handwritten digit recognition by support vector machine optimized by Bat algorithm. In: WSCG ’2016: short commu nications proceedings: The 24th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2016 in cooperation with EUROGRAPHICS. Plzen, Czech Republic: University of West Bohemia, pp.369- 376, May 30-June 03. Available at: http://hdl.handle.net/11025/29725 [Accessed: 10 March 2022].
Wang, H., Zheng, B., Yoon, S.W. & Ko, H.S. 2018. A support vector machine-based ensemble algorithm for breast cancer diagnosis. European Journal of Operational Research, 267(2), pp. 687–699. Available at: https://doi.org/10.1016/j.ejor.2017.12.001
Wang, K. & Zhang, H. 2020. A Novel Naive Bayesian Approach to Inference with Applications to the MNIST Handwritten Digit Classification. In: 2020 International Conference on Computational Science and Computational Intelligence (CSCI). Las Vegas, NV, USA, pp.1354-1358, December 16-18. Available at: https://doi.org/10.1109/CSCI51800.2020.00252
Yuan, G.X., Ho, C.H. & Lin, C.J. 2012. Recent Advances of Large-Scale Linear Classification. Proceedings of the IEEE, 100(9), pp. 2584–2603. Available at: https://doi.org/10.1109/JPROC.2012.2188013
Zhai, Z., Xu, Z., Zhou, X., Wang, L. & Zhang, J. 2015. Recognition of hazard grade for cotton blind stinkbug based on Naive Bayesian classifier. Transactions of the Chinese Society of Agricultural Engineering, 31(1), pp. 204–211 [online]. Available at: http://www.tcsae.org/nygcxben/article/abstract/20150128 [Accessed: 1 March 2022].
Zhu, W., Yeh, W., Chen, J., Chen, D., Li, A. & Lin, Y. 2019. Evolutionary Convolutional Neural Networks Using ABC. In: ICMLC ’19: Proceedings of the 2019 11th International Conference on Machine Learning and Computing. Zhuhai, China, pp.156–162, February 22-24. Available at: https://doi.org/10.1145/3318299.3318301
Copyright (c) 2023 Ira M. Tuba, Una M. Tuba, Mladen Đ, Veinović
This work is licensed under a Creative Commons Attribution 4.0 International License.
Proposed Creative Commons Copyright Notices
Proposed Policy for Military Technical Courier (Journals That Offer Open Access)
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).