Handwritten English Digit Recognition: A Machine Learning Formulation
Suchismita Behera1, Niva Das2
1Suchismita Behera , Asst. Prof. in the department of ECE, SOA(deemed to be university) since.
2Niva Das , Prof. in the department of ECE, SOA (deemed to be University) since.

Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 6055-6058 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8634118419/2019©BEIESP | DOI: 10.35940/ijrte.D8634.118419

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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.
Keywords: Numeral Recognition, HOG, SVM, single decision tree classifier.
Scope of the Article: Machine Learning.