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Multi-font Telugu Text Recognition Using Hidden Markov Models and Akshara Bi-grams

  • Koteswara Rao DevarapalliEmail author
  • Atul Negi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10481)

Abstract

Recent advances in the information technology made possible to introduce many Unicode Telugu fonts for the documentation needs of present society. But the recognition of documents printed in a variety of fonts poses new challenges in building Telugu OCR systems. In this paper, we demonstrate multi-font Telugu printed word recognition using implicit segmentation approach that provides segmentation as a by-product of recognition. Our word recognition approach relies on Hidden Markov Models and akshara bi-gram language model to recognize word images in terms of aksharas (characters). The training set of word images is prepared from document images of popular books and the synthetic document images generated using 8 different Unicode fonts. The testing involves matching the feature vector sequence against sequence of akshara HMMs based on bi-grams. The CER and WER of this system are 21% and 37% respectively. The performance of our system is very encouraging.

Keywords

Akshara Bi-gram DCT HMM Telugu OCR Word recognition 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringMahatma Gandhi Institute of TechnologyHyderabadIndia
  2. 2.School of Computer and Information SciencesUniversity of HyderabadGachibowli, HyderabadIndia

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