Handwritten Digit Recognition Using Machine Learning: A Review
Anchit Shrivastava, Isha Jaggi, Sheifali Gupta, Deepali Gupta · 2019 2nd International Conference on Power Energy, Environment and Intelligent Control (PEEIC) · 2019
The task for handwritten digit recognition has been troublesome due to various variations in writing styles. Therefore, we have tried to create a base for future researches in the area so that the researchers can overcome the existing problems. The existing methods and techniques for handwritten digit recognition were reviewed and understood to analyze the most suitable and best method for digit recognition. A number of 60,000 images were used as training sets of images with pixel size of 28×28. The images/training sets were matched with original image. It was found out after complete analysis and review that classifier ensemble system has the least error rate of just 0.32%. In this paper, review of different methods handwritten digit recognition were observed and analyzed.