Handwritten Digit Recognition Using Machine Learning Algorithms

S M Shamim (Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh, Tangail-1902, Bangladesh)
Mohammad Badrul Alam Miah (Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh, Tangail-1902, Bangladesh)
Angona Sarker (Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh, Tangail-1902, Bangladesh)
Masud Rana (Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh, Tangail-1902, Bangladesh)
Abdullah Al Jobair (Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh, Tangail-1902, Bangladesh)

Indonesian Journal of Science and Technology
Vol 3, No 1 (2018): IJoST: VOLUME 3, ISSUE 1, April 2018

Handwritten character recognition is one of the practically important issues in pattern recognition applications. The applications of digit recognition include in postal mail sorting, bank check processing, form data entry, etc. The main problem lies within the ability on developing an efficient algorithm that can recognize hand written digits, which is submitted by users by the way of a scanner, tablet, and other digital devices. This paper presents an approach to off-line handwritten digit recognition based on different machine learning techniques. The main objective of this paper is to ensure the effectiveness and reliability of the approached recognition of handwritten digits. Several machines learning algorithms (i.e. Multilayer Perceptron, Support Vector Machine, Naïve Bayes, Bayes Net, Random Forest, J48, and Random Tree) have been used for the recognition of digits using WEKA. The experimental results showed that the highest accuracy was obtained by Multilayer Perceptron with the value of 90.37%.