Affiliation:
1. Department of Electronics and Communication Engineering, Ramaiah Institute of Technology, Bangalore, India
Abstract
Recognition of Kannada Characters is a complex task as the number of classes in Kannada language by considering all combinations of vowels and consonants is 623,893. In this paper, the complexity is reduced from 623,893 to just having 313 classes as Main aksharas (Vowel, Consonants,Vowel modifiers and Consonant modifiers) and 30 classes as vattu aksharas(conjuncts) by using two line segmentation. A novel CNN model for recognition of printed and handwritten Kannada characters is proposed. CNN model with two, three and four layers are designed for Main akshara and Vattu aksharas with different filter size. The database consists of total of 31,300 samples and 3000 samples of printed and handwritten characters of Main akshara and Vattu aksharas respectively. Simulation result revealed that CNN model with four layer architecture is the best model for recognition of Kannada characters. This model achieved a recognition accuracy of 98.83% and 99.29% for printed Main akshara and Vattu aksharas and 82.50% and 80.92% for handwritten main and vattu akshara respectively.
Subject
Artificial Intelligence,General Engineering,Statistics and Probability
Reference14 articles.
1. Naveena C. and ManjunathAradhya V.N. , Handwritten character segmentation for Kannada scripts, World Congress on Information and Communication Technologies, IEEE, 2012.
2. SavitaAhlawat, AmitChoudhary, Anand Nayyar, SaurabhSingh and ByungunYoon, Improved handwritten digit recognition using convolutional neural networks (CNN), Sensors 20(12) (2020), 3344.
3. Chaiathra D. and Indira K. , Handwritten Online Character Recognition for Single Stroke Kannada Characters, 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology, 2017.
4. Netravati Belagali and Shanmukhappa A. Angadi, OCR for Handwritten Kannada Language Script, International Journal of Recent Trends in Engineering and Research, ISSN(online): 2455 (2016), 1457.
5. Abhishek S. Rao, S. Sandhya, K. Anusha, Arpitha, ChandanaNayak and Meghana, SnehaNayak, Exploring Deep Learning Techniques for Kannada Handwritten Character Recognition: A Boon for Digitization, International Journal of Advanced Science and Technology 29(5) (2020), 11078–11093.