Offline Handwriting Recognition on Hindi using CNN RNN Hybrid Network
A. Balamurali1, Nacode Vidheesh Kumar2, Aniket Yadav3, Rushikesh Deshmukh4
1Nacode Vidheesh Kumar, B.Tech Student, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
2Aniket Yadav, B.Tech Student, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
3Rushikesh Deshmukh, B.Tech Student, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
4A. Balamurali, B.Tech Student, Department of Computer Science and Engineering, 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: 1746-1748 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6184048419/19©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: Recognising the Scripts written in Hindi is an onerous task due to the delicacies and variations in the script. Handwriting of every script is diversified in writing styles and orientation angles. This requires a neural network with a large training dataset to successfully recognise the script. Till now the development of this handwriting recognisers have been obstructed due to the unavailability of hand-written public datasets in Hindi. In this paper we used CNN RNN hybrid network based on IIIT-HW-Dev dataset to differentiate and recognise handwritten scripts.
Keywords: CNN-RNN Hybrid Network, Handwriting Recognition, Hindi Handwriting
Scope of the Article: Pattern Recognition