Handwritten Digit Recognition Using Logistic Regression, SVM, KNN and CNN Algorithms

Authors

  • K. Sujana Kumari
  • G. Murali

Keywords:

Support vector machine, KNN, logistic regression, CNN, handwritten digit images

Abstract

The style of handwriting varies from person to person. Handwritten numbers are not always the same size, orientation and width. To develop a system to understand this, the machine recognizes handwritten digit images and classifies them into 10 digits (from 0 to 9). The recognition of handwritten digits is a technology which is used for the automatic recognizing and detecting handwritten digital data through various deep lerning and machine learningmodels. This paper uses a different machine learning algorithms to improve productivity anda variety of models to reduce complexity. Machine Learning is a subset of Artificial Intelligence, actually Machine Learningapplications which learns from previous experiences and it automatically improves with the previous experiences. This paper is about recognizing handwritten digits from 0 to 9 from the familiar Modified National Institute of Standards and Technology (MNIST) dataset, then comparision takes place between machine learning algorithms like Support Vector Machine(SVM), Logistic Regression, K-NearestNeighbor (KNN) and deep learning algorithm like CNN.

References

Wang Y, Wang R, Li D, et al. Improved Handwritten Digit Recognition using Quantum K-Nearest Neighbor Algorithm. Int J Theor Phys. 2019; 58(4): 2331–2340.

Khan H. MCS HOG Features and SVM Based Handwritten Digit Recognition System. Journal of Intelligent Learning Systems and Applications. 2017; 9(2): 21–33.

Ge D, Yao X, Xiang W, Wen X, Liu E. 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; 658–662. doi: 10.1109/ICICTA49267.2019.00145.

Al-Wzwazy Haider, et al. Handwritten Digit Recognition Using Convolutional Neural Networks. International Journal of Innovative Research in Computer and Communication Engineering. 2016; 4(2): 1101–1106.

Vijayalaxmi BK, Rudraswamimath R. Handwritten Digit Recognition using CNN. International Journal of Innovative Science and Research Technology. 2019 Jun; 4(6): 182–187.

Assegie TA, Nair PS. Handwritten digits recognition with decision tree classification: a machine learning approach. International Journal of Electrical and Computer Engineering (IJECE). 2019 Oct 1; 9(5): 4446–51.

Zufar Kayumov, DT. Convolution Neural Network Learning Features for Handwritten Digit Recognition. 2020 IEEE East-West Design & Test Symposium (EWDTS). 2020; 1–5.

Junyi Tang, PH, DL. Adhesive Handwritten Digit Recognition Algorithm Based on Improved Convolutional Neural Network. IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS). 2020; 388–392.

Mustafa Ali Abuzaraida, AMZ, AMZ. Online Recognition System for Handwritten Hindi Digits Based on Matching Alignment Algorithm. 2014 3rd IEEE International Conference on Advanced Computer Science Applications and Technologies. 2014; 168–171.

Anchit Shrivastava, IJ, SG, DG. Handwritten Digit Recognition Using Machine Learning: A Review. 2019 2nd International Conference on Power Energy, Environment and Intelligent Control (PEEIC). 2019; 322–326.

Rajalakshmi M, PS, PS. Pattern Recognition - Recognition of Handwritten Document Using Convolutional Neural Networks. 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS). 2019; 1–7.

Mahesh B. Machine Learning Algorithms A Review. International Journal of Science and Research. 2018; 9(1): 381–386.

Nurseitov Daniyar, BK, KM, AA. Classification of handwritten names of cities using various deep learning models. IEEE 15th International Conference on Electronics Computer and Computation (ICECCO). 2019; 1–4.

Fathima Siddique, SS, MABS. Recognition of Handwritten Digit using Convolutional Neural Network in python with Tensorflow and Comparison of Performance for Various Hidden Layers. 2019 IEEE 5th International Conference on Advances in Electrical Engineering (ICAEE). 2019; 541–546.

Pradeep J, ES, et al. Neural Network based Handwritten Character Recognition system without feature extraction. IEEE International Conference on Computer, Communication and Electrical Technology (ICCCET). 2011; 40–44.

Ray S. A Quick Review of Machine Learning Algorithms. 2019 IEEE International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). 2019; 35–39.

Published

2022-12-20

How to Cite

Kumari, K. S. ., & Murali, G. . (2022). Handwritten Digit Recognition Using Logistic Regression, SVM, KNN and CNN Algorithms. JOURNAL OF OPERATING SYSTEMS DEVELOPMENT &Amp; TRENDS, 9(2), 20–28. Retrieved from https://stmcomputers.stmjournals.com/index.php/JoOSDT/article/view/390