Telugu and Hindi Script Recognition using Deep learning Techniques
P. Sujatha1, D. Lalitha Bhaskari2

1P. Sujatha, Department of Computer Science & Engineering, Andhra University College of Engineering (Autonomous), Andhra University, Visakhapatnam, India.
2D. Lalitha Bhaskari, Department of Computer Science & Engineering, Andhra University College of Engineering (Autonomous), Andhra University, Visakhapatnam, India

Manuscript received on 22 August 2019. | Revised Manuscript received on 02 September 2019. | Manuscript published on 30 September 2019. | PP: 1758-1764 | Volume-8 Issue-11, September 2019. | Retrieval Number: K17550981119/2019©BEIESP | DOI: 10.35940/ijitee.K1755.0981119
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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 need for offline handwritten character recognition is intense, yet difficult as the writing varies from person to person and also depends on various other factors connected to the attitude and mood of the person. However, we are able to achieve it by converting the handwritten document into digital form. It has been advanced with introducing convolutional neural networks and is further productive with pre-trained models which have the capacity of decreasing the training time and increasing accuracy of character recognition. Research in recognition of handwritten characters for Indian languages is less when compared to other languages like English, Latin, Chinese etc., mainly because it is a multilingual country. Recognition of Telugu and Hindi characters are more difficult as the script of these languages is mostly cursive and are with more diacritics. So the research work in this line is to have inclination towards accuracy in their recognition. Some research has already been started and is successful up to eighty percent in offline hand written character recognition of Telugu and Hindi. The proposed work focuses on increasing accuracy in less time in recognition of these selected languages and is able to reach the expectant values.
Keywords: Offline handwritten character recognition, convolutional neural networks, latin, hindi, Chinese, telugu, English
Scope of the Article: