Handwritten recognition of Tamil vowels using deep learning
N Ram Prashanth,B Siddarth,Anirudh Ganesh,Vaegae Naveen Kumar +3 more
- Vol. 263, Iss: 5, pp 052035
TLDR
This paper explored the performance of Deep Belief Networks in the classification of Handwritten Tamil vowels, and conclusively compared the results, and proposed method has shown satisfactory recognition accuracy in light of difficulties faced with regional languages.Abstract:
We come across a large volume of handwritten texts in our daily lives and handwritten character recognition has long been an important area of research in pattern recognition. The complexity of the task varies among different languages and it so happens largely due to the similarity between characters, distinct shapes and number of characters which are all language-specific properties. There have been numerous works on character recognition of English alphabets and with laudable success, but regional languages have not been dealt with very frequently and with similar accuracies. In this paper, we explored the performance of Deep Belief Networks in the classification of Handwritten Tamil vowels, and conclusively compared the results obtained. The proposed method has shown satisfactory recognition accuracy in light of difficulties faced with regional languages such as similarity between characters and minute nuances that differentiate them. We can further extend this to all the Tamil characters.read more
Citations
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References
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Journal ArticleDOI
A multi-objective approach towards cost effective isolated handwritten Bangla character and digit recognition
TL;DR: A multi-objective region sampling methodology for isolated handwritten Bangla characters and digits recognition has been proposed and an AFS theory based fuzzy logic is utilized to develop a model for combining the pareto-optimal solutions from two multi- objective heuristics algorithms.
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