Recognition of Off-Line Handwritten Rajasthani Characters using Generalized Feed Forward Classifier
S E Warkhede1, S. K. Yadav2, V M Thakare3, P E Ajmire4
1S.E. Warkhede*, Assistant Professor and Head in Dept. of Computer Science, Vidnyan Mahavidyalaya, Malkapur (M.S.), India.
2Dr. S. K, Yadav, Research Director in Shri JJT University, Churu, Rajasthan.
3Dr. Vilas M. Thakare , Professor, Head in Computer Science, Faculty of Engineering & Technology, Post Graduate Department of Computer Science, Sant Gadgebaba Amravati University, Amravati.
4Dr. Prafulla E. Ajmire , head of department of Computer Science at G S Science, Arts & Commerce College, Khamgaon. (M.S.), India.
Manuscript received on November 18, 2019. | Revised Manuscript received on 28 November, 2019. | Manuscript published on December 10, 2019. | PP: 3230-3233 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7884129219/2019©BEIESP | DOI: 10.35940/ijitee.B7884.129219
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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: The offline handwritten identification in the area of pattern recognition was a heavy and difficult task. Because of its application in different areas, a set of work is being done and the results are continuing to be strengthened by different methods. We suggested in this paper a handwritten model for individual character recognition using generalized neural networks for feed forward. We take 17 character samples handwritten in scanned image format for experimental purposes; Rajasthani knows 850 different samples of handwritten characters. HOG extraction methods are used to construct pattern vectors for all training sets. These features are recognition classifier for generalized feed forward. We obtained an overall classification with GFF classifier accuracy rate of 85.21% from the proposed scheme for the identification of Rajasthani characters.
Keywords: Handwritten Character Recognition, Feature Extraction, GFF, Classifier
Scope of the Article: Pattern Recognition