Open AccessHandwritten English Digit Recognition: A Machine Learning Formulation
Author(s) -
Santosh Kumar Behera,
Niva Das
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d8634.118419
Subject(s) - digit recognition , computer science , numeral system , classifier (uml) , artificial intelligence , pattern recognition (psychology) , feature extraction , speech recognition , handwriting , computation , support vector machine , decision tree , handwriting recognition , intelligent character recognition , machine learning , character recognition , artificial neural network , algorithm , image (mathematics)
Handwriting recognition is a challenging machine learning task. Handwritten Recognition (HR) systems have become commercially popular due to their potential applications. The challenges that arise due to wide range of variations in shape, structure ,size and individual writing style can be handled with the combination of a powerful feature extraction technique and an efficient classifier. In this paper, an attempt has been made to compare four different feature extraction cum classifier schemes for English handwritten numeral recognition in terms of computational time and accuracy of recognition. Observations show that single decision tree requires less computation time while SVM yields better accuracy.