A Comprehensive Data Analysis on Handwritten Digit Recognition using Machine Learning Approach
Meer Zohra1, D.Rajeswara Rao2
1Meer Zohra, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur (Andhra Pradesh), India.
2D.Rajeswara Rao, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur (Andhra Pradesh), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 1449-1453 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3888048619/19©BEIESP
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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 one of the compelling research works going on because every individual in this world has their own style of writing. It is the capability of the computer to identify and understand handwritten digits or characters automatically. Because of the progress in the field of science and technology, everything is being digitalized to reduce human effort. Hence, there comes a need for handwritten digit recognition in many real-time applications. MNIST data set is widely used for this recognition process and it has 70000 handwritten digits. Many Machine Learning and Deep Learning Algorithms are developed which can be used for this digit classification. This paper performs the analysis of accuracies and performance measures of algorithms Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNN).
Keyword: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Convolutional Neural Networks (CNN).
Scope of the Article: Machine Learning