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Masud Rana
Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh, Tangail-1902, Bangladesh
Computer Science & IT Environmental Science

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Handwritten Digit Recognition Using Machine Learning Algorithms S M Shamim; Mohammad Badrul Alam Miah; Angona Sarker; Masud Rana; Abdullah Al Jobair
Indonesian Journal of Science and Technology Vol 3, No 1 (2018): IJoST: VOLUME 3, ISSUE 1, April 2018
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/ijost.v3i1.10795

Abstract

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%.
Co-Authors Abdullah Al Jobair Angona Sarker Mohammad Badrul Alam Miah S M Shamim