Handwritten Digit Recognition Accuracy Comparison of Various Techniques
Arun.V1, Abinesh P2, Akash Kumar S3, Arvind Kumar D.R4
1Arun V, Assistant Professor, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
2Abinesh P, UG Scholars, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
3Akash Kumar S, UG Scholars, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
4Arvind Kumar D R, UG Scholars, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 1499-1502 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6305048419/19©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: Hand written characters and numbers recognition methodology has sought more attention among the various developed learnings and algorithm in today’s current scenario. The learnings which are talking about are a machine and deep learning in this paper, we used a calculation of machine learning certain methods like KNN, RFC and SVM and made a comparison with deep learning CNN method calculation with help of utilizing tensor flow, keras and theano. Utilizing the above techniques we get a more accurate result on CNN using theano as contrasted with using SVM, KNN and RFC.
Keywords: Multilayer CNN, Supervised Vector Machine, K-Nearest Neighbors, Random Forest Classifier.
Scope of the Article: Pattern Recognition