Handwriting Text Recognition using Neural Networks
Parikshith H1, Naga Rajath S M2, Shwetha D3, Sindhu C M4, Ravi P5
1Parikshith H*, Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India.
2Naga Rajath S M, Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India.
3Shwetha D, Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India.
4Sindhu C M, Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India.
5Ravi P, Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India.
Manuscript received on November 14, 2019. | Revised Manuscript received on 23 November, 2019. | Manuscript published on December 10, 2019. | PP: 4088-4092 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7705129219/2019©BEIESP | DOI: 10.35940/ijitee.B7705.129219
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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: Hand written text recognition is a laborious task because humans can write a similar message in numerous ways or due to huge diversity in individual’s style of writing. The performance of text recognition systems implemented as neural networks has better results and accuracy than normal traditional classifiers. In this paper we explore the methods used to recognize and detect handwritten text or words in different languages. The major method used to recognize text is the Convolutional neural network (CNN) as a deep learning classifier. The other techniques used are Recurrent Neural Network (RNN) and a custom developed model called deep-writer, which is a variant of CNN architecture.
Keywords: Handwritten Text Recognition, Deep Learning, Convolutional Neural Networks.
Scope of the Article: Deep Learning