Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Computer Vision, Graphics, and Image Processing (ICVGIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10481))

  • 1359 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bazzi, I., Schwartz, R., Makhoul, J.: An omnifont open-vocabulary OCR system for English and Arabic. IEEE Trans. Pattern Anal. Mach. Intell. 21(6), 495–504 (1999)

    Article  Google Scholar 

  2. Elms, A., Procter, S., Illingworth, J.: The advantage of using an HMM-based approach for faxed word recognition. Int. J. Doc. Anal. Recogn. 1(1), 18–36 (1998)

    Article  Google Scholar 

  3. Khorsheed, M.S.: Offline recognition of omnifont Arabic text using the HMM toolkit (HTK). Pattern Recogn. Lett. 28(12), 1563–1571 (2007)

    Article  Google Scholar 

  4. Krishnan, P., Sankaran, N., Singh, A.K., Jawahar, C.V.: Towards a robust OCR system for Indic scripts. In: 2014 11th IAPR International Workshop on Document Analysis Systems (DAS), pp. 141–145, April 2014

    Google Scholar 

  5. Kumar, P.P., Bhagvati, C., Negi, A., Agarwal, A., Deekshatulu, B.L.: Towards improving the accuracy of Telugu OCR systems. In: ICDAR, pp. 910–914. IEEE Computer Society (2011)

    Google Scholar 

  6. Lam, L., Lee, S.-W., Suen, C.: Thinning methodologies-a comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 14(9), 869–885 (1992)

    Article  Google Scholar 

  7. Natarajan, P., Lu, Z., Schwartz, R., Bazzi, I., Makhoul, J.: Multilingual machine printed OCR. Int. J. Pattern Recogn. Artif. Intell. 15(01), 43–63 (2001)

    Article  Google Scholar 

  8. Natarajan, P., MacRostie, E., Decerbo, M.: The BBN byblos Hindi OCR system. In: Govindaraju, V., Setlur, S. (eds.) Guide to OCR for Indic Scripts. Advances in pattern recognition, pp. 173–180. Springer, London (2010). doi:10.1007/978-1-84800-330-9_9

    Google Scholar 

  9. Negi, A., Bhagvati, C., Krishna, B.: An OCR system for Telugu. In: ICDAR, pp. 1110–1114. IEEE Computer Society (2001)

    Google Scholar 

  10. Rabiner, L.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)

    Article  Google Scholar 

  11. Rasagna, V., Jinesh, K.J., Jawahar, C.V.: On multifont character classification in Telugu. In: Singh, C., Singh Lehal, G., Sengupta, J., Sharma, D.V., Goyal, V. (eds.) ICISIL 2011. CCIS, vol. 139, pp. 86–91. Springer, Heidelberg (2011). doi:10.1007/978-3-642-19403-0_14

    Chapter  Google Scholar 

  12. Roy, P., Roy, S., Pal, U.: Multi-oriented text recognition in graphical documents using HMM. In: 2014 11th IAPR International Workshop on Document Analysis Systems (DAS), pp. 136–140, April 2014

    Google Scholar 

  13. Vasantha Lakshmi, C., Patvardhan, C.: A multi-font OCR system for printed Telugu text. In: 2002 Proceedings of Language Engineering Conference, pp. 7–17, December 2002

    Google Scholar 

  14. Wu, Y., Shivakumara, P., Wei, W., Lu, T., Pal, U.: A new ring radius transform-based thinning method for multi-oriented video characters. IJDAR 18(2), 137–151 (2015)

    Article  Google Scholar 

  15. Young, S., Evermann, G., Gales, M., Hain, T., Kershaw, D., Liu, X.A., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., Woodland, P.: The HTK Book (for HTK Version 3.4). Cambridge University Engineering Department (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Hyderabad, 500075, India

    Koteswara Rao Devarapalli

  2. School of Computer and Information Sciences, University of Hyderabad, Gachibowli, Hyderabad, 500046, India

    Koteswara Rao Devarapalli & Atul Negi

Authors
  1. Koteswara Rao Devarapalli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Atul Negi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Koteswara Rao Devarapalli .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology, Chittoor, India

    Snehasis Mukherjee

  2. GE Global Research, Bangalore, India

    Suvadip Mukherjee

  3. Indian Statistical Institute, Kolkata, India

    Dipti Prasad Mukherjee

  4. International Institute of Information Technology, Hyderabad, India

    Jayanthi Sivaswamy

  5. Indian Institute of Technology Bombay, Mumbai, India

    Suyash Awate

  6. CEDAR, Buffalo, New York, USA

    Srirangaraj Setlur

  7. International Institute of Information Technology, Hyderabad, India

    Anoop M. Namboodiri

  8. CSIR-CEERI, Pilani, Rajasthan, India

    Santanu Chaudhury

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Devarapalli, K.R., Negi, A. (2017). Multi-font Telugu Text Recognition Using Hidden Markov Models and Akshara Bi-grams. In: Mukherjee, S., et al. Computer Vision, Graphics, and Image Processing. ICVGIP 2016. Lecture Notes in Computer Science(), vol 10481. Springer, Cham. https://doi.org/10.1007/978-3-319-68124-5_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68124-5_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68123-8

  • Online ISBN: 978-3-319-68124-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation