Title: Offline Handwritten Telugu Character Dataset and Recognition Author: Atul Negi{2}, Anish M Rao{1}
Affiliation: {1}PES University, India; {2}University of Hyderabad, India
Abstract: Note: Track chosen is Image Processing because the main focus of the paper is the dataset creation process. However, the recognition approach uses Deep Learning and hence is more related to Track 2 or 3. Summary: Since Telugu is a widely spoken language, an exhaustive database of handwritten Telugu characters becomes a necessity to drive progress in tasks like handwriting recognition, writer recognition, etc in the Telugu language. This work produces such a database with real-world offline handwritten characters extracted from scanned documents, making it the largest and most varied database in this domain. The method of collecting data, preprocessing steps, as well as the extraction approach to obtain individual Telugu characters is explained in detail. This work also presents a method of handwritten Telugu character recognition using Convolutional Neural Networks as a baseline classifier, as well as Visual Attention Networks as a more advanced solution. Finally, the proposed architecture is compared with previous approaches and it is shown that it outperforms solutions that consider a similar number of classes.
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