Pattern Recognition using Convolutional Neural Network for Handwritten Gujarati Numerals
G. D. Upadhye1, Sanika M. Shirsat2, Sayali R. Shinde3, Megha A. Sonawane4, Komal D. Pandit5
1Gopal D. Upadhye, Assistant Professor, Department of Computer Engineering, Rajarshi Shahu College of Engineering, Tathawade, Pune, India.
2Sanika Shirsat, Pursuing M.Tech, Department of Computer Engineering, Savitribai Phule Pune, India.
3Komal Pandit, B.E, Department of Computer Engineering, Savitribai Phule Pune, India.
4Sayali Shirsat, B.E Department of Computer Engineering, Savitribai Phule Pune, India.
5Megha Sonawane, B.E, Department of Computer Engineering, Savitribai Phule Pune, India.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 2680-2684 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6042018520/2020©BEIESP | DOI: 10.35940/ijrte.E6042.018520
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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: Day-to-day technology is going in no time, digital acknowledgement are taking part immensely and providing additional scope to perform scrutiny in CNN techniques. Recognition of Gujarati transcribed numeral is difficult compared to different western numerals. However, several analyzers have provided real time answer for transcribed Gujarati numerals. This paper represents acknowledgement of transcribed Gujarati digits which enhances Convolutional Neural Network. Current analysis offers several solutions on Gujarati handwritten documents analysis and reasonable accuracy for concerning transcribed digit recognition.
Keywords: Gujarati Handwriting; Gujarati Numerals; Handwritten Numerals, Convolution Neural Network, CNN, and Data-Set.
Scope of the Article: Pattern Recognition and Analysis.