Hand Written Digits Classification Through Multi-Classifier Bag of Visual Words
R. Hariharan1, M.Dhilsath Fathima2, Vibek Jyoti Roy3, Yasir Ahmad Khan4
1Hariharan. R, Department of Information Technology, VelTech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Avadi, Chennai (Tamil Nadu), India.
2Dhilsath Fathima. M, Department of Information Technology, VelTech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Avadi, Chennai (Tamil Nadu), India.
3Vibek Jyoti Roy, Department of Information Technology, VelTech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Avadi, Chennai (Tamil Nadu), India.
4Yasir Ahmad Khan, Department of CSE, VelTech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Avadi, Chennai (Tamil Nadu), India.
Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 140-144 | Volume-8 Issue-7, May 2019 | Retrieval Number: F4021048619/19©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: Today our world moving towards the smart technology in many ways. In this smart world we are making everything easy. Instead of typing with hand we can convert our hand-written letters to text format. There are many technology’s available to recognize handwriting and convert into text format, but still many cases are getting flaw in accurate prediction. Many machine learning classifiers available for recognize and classifies hand written digit. Bag of visual words is one of the simple classification method. Bag of features is detecting the surface and finding features of image and creating a vocabulary with visual words. This paper propose a multi-classifier bag of features methodology to identify hand-written digits.
Keyword: Bag of visual Words ,Bag of Features, HDR, Multi Classifiers.
Scope of the Article: Classification.