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Review of Optical Devanagari Character Recognition Techniques

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Intelligent System Design

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1171))

Abstract

Optical character recognition techniques are capable of automatic translating of document images into equivalent character codes, so it helps in saving human energy as well as cost. These techniques can play a key role to improve or enhance the interaction between human and machine in many applications such as postal automation, signature verification, recognition of city names and automatic bank cheque processing/reading. This paper gives a review of various techniques explored for Devanagari word/text and isolated character recognition in the past few years. Different challenges to optical character recognition are also presented in this work. In the end, practical aspects towards the development of a robust optical character recognition system has been discussed along with directions for future research.

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References

  1. Pal, U., Roy, K., & Kimura, F. (2008). Bangla handwritten pin code string recognition for Indian postal automation. In 11th International Conference on Frontiers in Handwriting Recognition (pp. 290–295). Canada: L. Lam Publications.

    Google Scholar 

  2. Pal, U., Roy, K., & Kimura, F. (2009). A lexicon-driven handwritten city-name recognition scheme for Indian postal automation. IEICE Transactions on Information and Systems, 92(5), 1146–1158.

    Article  Google Scholar 

  3. Jayadevan, R., Kolhe, S. R., Patil, P. M., & Pal, U. (2011). Offline recognition of devanagari script: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C Applications and Reviews, 41(6), 782–786.

    Article  Google Scholar 

  4. Sethi, I. K., & Chatterjee, B. (1977). Machine recognition of constrained hand printed Devanagari. Pattern Recognition, 9(2), 69–75.

    Article  Google Scholar 

  5. Sarkhel, R., Das, N., Das, A., Kundu, M., & Nasipuri, M. (2017). A multi-scale deep quad tree based feature extraction method for the recognition of isolated handwritten characters of popular indic scripts. Pattern Recognition, 71, 78–93.

    Article  Google Scholar 

  6. Chaudhuri, B. B., & Pal, U. (1997). Skew angle detection of digitized indian script documents. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(2), 182–186.

    Article  Google Scholar 

  7. Liang, J., DeMenthon, D., & Doermann, D. (2008). Geometric rectification of camera-captured document images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(4), 591–605.

    Article  Google Scholar 

  8. Singh, P. K., Sarkar, R., & Nasipuri, M. (2015). Offline script identification from multilingual indic-script documents: A state-of-the-art. Computer Science Review, 15(16), 1–28.

    Article  MathSciNet  Google Scholar 

  9. Kumar, S. (2016). A study for handwritten devanagari word recognition. In IEEE International Conference on Communication and Signal Processing (pp. 1009–1014). Melmaruvathur: IEEE Press.

    Google Scholar 

  10. Garg, N. K., Kaur, L., & Jindal, M. K. (2015). Recognition of handwritten hindi text using middle region of the words. International Journal of Software Innovation, 3(4), 62–71.

    Article  Google Scholar 

  11. Jayadevan, R., Kolhe, S. R., Patil, P. M., & Pal, U. (2011). Automatic processing of handwritten bank cheque images: A survey. International Journal on Document Analysis and Recognition, 15(4), 267–296.

    Article  Google Scholar 

  12. Parui, S. K., & Shaw, B. (2007). Offline handwritten Devanagri word recognition: An HMM based approach. In A. Ghose, R. K. De, & S. K. Pal (Eds.), PReMI (Vol. 4815, pp. 528–535). Berlin: Springer-verlag, LNCS.

    Google Scholar 

  13. Shaw, B., Parui, S. K., & Shridhar, M. (2008). Offline handwritten Devanagari word recognition: A holistic approach based on directional Chain code feature and HMM. In IEEE International Conference of Information Technology (pp. 203–208). Bhubaneswar: IEEE Press.

    Google Scholar 

  14. Shaw, B., Parui, S. K., Shridhar, M. (2008). A segmentation based approach to offline handwritten Devanagri word recognition. In IEEE International Conference on Information Technology (pp. 256–257). Bhubaneswar: IEEE Press.

    Google Scholar 

  15. Singh, B., Mittal, A., Ansari, M. A., & Ghosh, D. (2011). Handwritten word recognition: A curvelet transform based approach. International Journal of Computer Science and Engineering Survey (IJCSES), 3(4), 1658–1665.

    Google Scholar 

  16. Ramachandrula, S., Jain, S., & Ravishankar, H. (2012). Offline handwritten word recognition in Hindi. In Workshop on Document Analysis and Recognition (pp. 49–54). New York: ACM.

    Google Scholar 

  17. Shaw, B., Bhattacharya, U., & Parui, S. K. (2015). Offline handwritten Devanagari word recognition: Information fusion at feature and classifier level. In IEEE 3rd IAPR Asian Conference on Pattern Recognition (pp. 720–724). Malaysia: IEEE Press.

    Google Scholar 

  18. Bhunia, A. K., Roy, P. P., Mohta, A., & Pal, U. (2018). Cross-language framework for word recognition and spotting of indic scripts. Pattern Recognition, 79, 12–31.

    Article  Google Scholar 

  19. Kale, K. V., Chavan, S. V., Kazi, M. M., & Rode, Y. S. (2013). Handwritten Devanagari compound character recognition using Legendre moment: An artificial neural network approach. In IEEE International Symposium on Computational and Business Intelligence (pp. 274–278). New Delhi: IEEE Press.

    Google Scholar 

  20. Kubatur, S., Sid-Ahmed, M., & Ahmadi, M. (2012). A neural network approach to online devanagari handwritten character recognition. In IEEE International Conference on High Performance Computing and Simulation (pp. 209–214). Madrid: IEEE Press.

    Google Scholar 

  21. Yadav, D., Sanchez-Cuadrado, S., & Morato, J. (2013). Optical character recognition for Hindi language using a neural-network approach. Journal of Information Processing Systems (JIPS), 9(1), 117–140.

    Article  Google Scholar 

  22. Pal, U., Chanda, S., Wakabayashi, T., & Kimura, F. (2008). Accuracy Improvement of Devnagari character recognition combining SVM and MQDF. In 11th International Conference on Frontiers in Handwriting Recognition (pp. 367–372). Canada: L. Lam Publications.

    Google Scholar 

  23. Jangid, M., & Srivastava, S. (2014). Gradient local auto-correlation for handwritten Devanagari character recognition. In IEEE International Conference on High Performance Computing and Applications (pp. 1–5). Bhubaneswar: IEEE Press.

    Google Scholar 

  24. Hanmandlu, M., Murthy, O. V. R., & Madasu, V. K. (2007). Fuzzy model based recognition of handwritten hindi characters. In 9th IEEE Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (pp. 454–461). Glenelg: IEEE Press.

    Google Scholar 

  25. Agrawal, P., Hanmandlu, M., & Lall, B. (2009). Coarse classification of handwritten hindi characters. International Journal of Advanced Science and Technology, 10, 43–54.

    Google Scholar 

  26. Dixit, A., Navghane, A., & Dandawate, Y. (2014). Handwritten devanagari character recognition using wavelet based feature extraction and classification scheme. In: Annual IEEE India Conference (pp. 1–4). Pune: IEEE Press.

    Google Scholar 

  27. Acharya, S., Pant, A. K., & Gyawali, P. K. (2015). Deep learing based large scale handwritten devanagari character recognition. 9th International Conference on Software, Knowledge, Information Management and Applications (pp. 1–6). Nepal: IEEE Press.

    Google Scholar 

  28. Dongre, V. J., & Mankar, V. H. (2015). Devanagari offline handwritten numeral and character recognition using multiple features and neural network classifier. In 2nd International Conference on Computing for Sustainable Global Development (pp. 425–431). New Delhi: IEEE Press.

    Google Scholar 

  29. Shelke, S., & Apte, S. (2016). Performance optimization and comparative analysis of neural networks for handwritten Devanagari character recognition. In IEEE International Conference on Signal and Information Processing (pp. 1–5). Vishnupuri: IEEE Press.

    Google Scholar 

  30. Jangid, M., & Srivastava, S. (2018). Handwritten Devanagari character recognition using layer-wise training of deep convolutional neural networks and adaptive gradient methods. Journal of Imaging, 4(14), 1–14.

    Google Scholar 

  31. Shaw, B., & Parui, S. K. (2010). A two stage recognition scheme for offline handwritten Devanagri words. In A. Ghosh, R. K. De, & S. K. Pal (Eds.), Research machine interpretation of patterns 2010. SSIR (Vol. 12, pp. 145–165). Singapore: World Scientific.

    Chapter  Google Scholar 

  32. Shaw, B., Bhattacharya, U., & Parui, S. K. (2014). Combination of features for efficient recognition of offline handwritten Devanagri words. In IEEE 14th International Conference on Frontier in Handwritten Recognition (pp. 240–245). Heraklion: IEEE Press.

    Google Scholar 

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Author information

Authors and Affiliations

  1. ECED, Giani Zail Singh Campus CET, MRSPTU, Bathinda, Punjab, India

    Sukhjinder Singh

  2. CSED, Giani Zail Singh Campus CET, MRSPTU, Bathinda, Punjab, India

    Naresh Kumar Garg

Authors
  1. Sukhjinder Singh
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  2. Naresh Kumar Garg
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Corresponding author

Correspondence to Sukhjinder Singh .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, KIIT Demmed to be University, Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  2. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. Department of Computer Science and Engineering, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India

    B. Janakiramaiah

  4. College of Information Science and Engineering, Ritsumeikan University, Kyoto, Japan

    Yen-Wei Chen

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Cite this paper

Singh, S., Garg, N.K. (2021). Review of Optical Devanagari Character Recognition Techniques. In: Satapathy, S., Bhateja, V., Janakiramaiah, B., Chen, YW. (eds) Intelligent System Design. Advances in Intelligent Systems and Computing, vol 1171. Springer, Singapore. https://doi.org/10.1007/978-981-15-5400-1_11

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