We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

Article:A Review of Research on Devnagari Character Recognition

by Vikas J Dongre, Vijay H Mankar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 2
Year of Publication: 2010
Authors: Vikas J Dongre, Vijay H Mankar
10.5120/1653-2224

Vikas J Dongre, Vijay H Mankar . Article:A Review of Research on Devnagari Character Recognition. International Journal of Computer Applications. 12, 2 ( December 2010), 8-15. DOI=10.5120/1653-2224

@article{ 10.5120/1653-2224,
author = { Vikas J Dongre, Vijay H Mankar },
title = { Article:A Review of Research on Devnagari Character Recognition },
journal = { International Journal of Computer Applications },
issue_date = { December 2010 },
volume = { 12 },
number = { 2 },
month = { December },
year = { 2010 },
issn = { 0975-8887 },
pages = { 8-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume12/number2/1653-2224/ },
doi = { 10.5120/1653-2224 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:00:37.269661+05:30
%A Vikas J Dongre
%A Vijay H Mankar
%T Article:A Review of Research on Devnagari Character Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 12
%N 2
%P 8-15
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

English Character Recognition (CR) has been extensively studied in the last half century and progressed to a level, sufficient to produce technology driven applications. But same is not the case for Indian languages which are complicated in terms of structure and computations. Rapidly growing computational power may enable the implementation of Indic CR methodologies. Digital document processing is gaining popularity for application to office and library automation, bank and postal services, publishing houses and communication technology. Devnagari being the national language of India, spoken by more than 500 million people, should be given special attention so that document retrieval and analysis of rich ancient and modern Indian literature can be effectively done. This article is intended to serve as a guide and update for the readers, working in the Devnagari Optical Character Recognition (DOCR) area. An overview of DOCR systems is presented and the available DOCR techniques are reviewed. The current status of DOCR is discussed and directions for future research are suggested.

References
  1. R.M.K. Sinha and Veena Bansal, "On Automating Trainer For Construction of Prototypes for Devnagari Text Recognition", Technical Report TRCS-95-232, I.I.T. Kanpur, India.
  2. R.M.K. Sinha and V. Bansal, "On Devnagari Document Processing", Int. Conf. on Systems, Man and Cybernetics, Vancouver, Canada, 1995.
  3. R.M.K. Sinha and Veena Bansal, “On Integrating Diverse Knowledge Sources in Optical Reading of Devnagari Script”.
  4. R.M.K.Sinha., “Rule Based Contextual Post-processing for Devnagari Text Recognition”, Pattern Recognition, Vol. 20, No. 5, pp. 475-485, 1987.
  5. R.M.K.Sinha, “On Partitioning a Dictionary for Visual Text Recognition”, Pattern Recognition, Vol 23, No. 5, pp 497-500, 1989.
  6. Veena Bansal and R.M.K. Sinha, "On Automating Generation of Description and Recognition of Devnagari Script using Strokes", Technical Report TRCS-96-241, I.I.T. Kanpur, India.
  7. R. M. K. Sinha, “A Journey from Indian Scripts Processing to Indian Language Processing”, IEEE Annals of the History of Computing, pp8-31, Jan–Mar 2009.
  8. R.G. Casey, D. R. Furgson, “Intelligent Forms Processing”, IBM System Journal, Vol. 29, No. 3, 1990.
  9. Anil K. Jain, Robert P.W. Duin, and Jianchang Mao, “Statistical Pattern Recognition: A Review”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, pp- 4-37, January 2000.
  10. George Negi, “Twenty years of Document analysis in PAMI”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 1, pp- 38-62, January 2000.
  11. U. Pal, B. B. Chaudhuri, ''Indian Script Character recognition: A survey'', Pattern Recognition, vol. 37, pp. 1887-1899, 2004.
  12. Rangachar Kasturi, Lawrence O’Gorman, Venu Govindaraju, “Document Image Analysis: A Primer”, Sadhana, Vol. 27, Part 1, pp. 3–22, February 2002.
  13. Ram Sarkar et al, “A Script Independent Technique for Extraction of Characters from Handwritten Word Images”, International Journal of Computer Applications Vol. 1, No. 23, 2010.
  14. Mohamed Cheriet, Nawwaf Kharma, Cheng-Lin Liu, Ching Y. Suen, “Character Recognition Systems: A Guide for students and Practioners”, John Wiley & Sons, Inc., Hoboken, New Jersey, 2007.
  15. U. Pal, T. Wakabayashi, F. Kimura, “Comparative Study of Devnagari Handwritten Character Recognition using Different Feature and Classifiers”, 10th Intl. Conf. on Document Analysis and Recognition, pp. 1111-1115, 2009.
  16. Richard Ishida, “An Introduction to Indic Scripts”, http://www.w3.org/2002/Talks/09-ri-indic/indic-paper.pdf.
  17. Richard G. Casey and Eric Lecoline, “A survey of methods and strategies in Character segmentation”.
  18. C. V. Jawahar, M. N. S. S. K. Pavan Kumar, S. S. Ravi Kiran, “A Bilingual OCR for Hindi-Telugu Documents and its Applications”, Seventh Intl. Conf. on Document Analysis and Recognition (ICDAR 2003) , pp 1-7, 2003.
  19. M. Hanmandlu and Pooja Agrawal, “A Structural Approach for Segmentation of Handwritten Hindi Text”, International Conference on Cognition and Recognition. pp 589-597.
  20. 21
  21. Rajiv Kapoor, Deepak Bagai, T. S. Kamal, “Skew angle detection of a cursive handwritten Devnagari script character image”, Journal of Indian Inst. Science, pp. 161–175, May-Aug. 2002
  22. U. Pal, M. Mitra and B. B. Chaudhuri, “Multi-Skew Detection of Indian Script Documents”, CVPRU IEEE, pp 292-296 , 2001.
  23. J. Serra, “Morphological Filtering: An Overview”, Signal Processing, vol. 38, no.1, pp.3-11, 1994.
  24. Nafiz Arica, Fatos T. Yarman-Vural, “An Overview of Character Recognition Focused On Off-line Handwriting”, C99-06-C-203, IEEE, 2000.
  25. I. S. Oh, J. S. Lee, C. Y. Suen, “Analysis of class separation and Combination of Class-Dependent Features for Handwriting Recognition”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol.21, no.10, pp.1089-1094, 1999.
  26. D. Trier, A. K. Jain, T. Taxt, “Feature Extraction Method for Character Recognition - A Survey”, Pattern recognition, vol.29, no.4, pp.641-662, 1996.
  27. G. G. Rajput, S. M. Mali,” Fourier Descriptor based Isolated Marathi Handwritten Numeral Recognition”, Int’l Journal of Computer Applications, Volume 3 – No.4, June 2010.
  28. U. Bhattacharya and B. B. Chaudhuri, “A Majority Voting Scheme for Multiresolution Recognition of Hand printed Numerals”, Seventh International Conference on Document Analysis and Recognition (ICDAR 2003).
  29. M.C. Padma, P.A.Vijaya, “Entropy Based Texture Features Useful for Automatic Script Identification”, International Journal on Computer Science and Engineering, Vol. 02, No. 02, 115-120, 2010.
  30. Muharrem Mercimek, Kayhan Gulez and Tarik Veli umcu, “Real object recognition using moment invariants”, Sadhana Vol. 30, Part 6, pp. 765–775, Dec. 2005.
  31. Harikesh Singh, R. K. Sharma, “Moment in Online Handwritten Character Recognition”, National Conference on Challenges & Opportunities in Information Technology (COIT-2007) Gobindgarh. March 23, 2007.
  32. J´erˆome Revaud, Guillaume Lavou´e and Atilla Baskurt, “Optimal similarity and rotation angle retrieval using Zernike moments”, July 16, 2007.
  33. S.V. Rajashekararadhya, P. Vanaja Ranjan, “A Novel Zone Based Feature Extraction Algorithm for Handwritten Numeral Recognition of Four Indian Scripts”, Digital Technology, Journal, Vol. 2, pp. 41-51, 2009.
  34. Sandhya Arora et al. “Recognition of Non-Compound Handwritten Devnagari Characters using a Combination of MLP and Minimum Edit Distance”, International Journal of Computer Science and Security (IJCSS), Volume-4, Issue 1.
  35. Santanu Chaudhury, Geetika Sethi, Anand Vyas, Gaurav Harit, “Devising Interactive Access Techniques for Indian Language Document Images”, (ICDAR 2003).
  36. Tapan K Bhowmik, Swapan K Parui Utpal Roy, “Discriminative HMM Training with GA for Handwritten Word Recognition”, IEEE, 2008.
  37. Latesh Malik, P.S. Deshpande , “Recognition of printed Devnagari characters with regular expression in finite state models”, International workshop on machine intelligence research, GHRCE Nagpur, India, 2009.
  38. M. Hanmandlu, O.V. Ramana Murthy, Vamsi Krishna Madasu, “Fuzzy Model based recognition of handwritten Hindi characters”, Digital Image Computing Techniques and Applications 0-7695-3067-IEEE. Feb-07.
  39. Reena Bajaj, Lipika Dey , Santanu Chaudhury, “Devnagari numeral recognition by combining decision of multiple connectionist classifiers”, Sadhana Vol. 27, Part 1, pp. 59–72, February 2002.
  40. Canasai Kruengkrai, Virach Sornlertlamvanich, Hitoshi Isahara, “ Language, Script, and Encoding Identification with String Kernel Classifiers”, Thai Computational Linguistics Laboratory, Thailand.
  41. P. S. Deshpande, Latesh Malik, Sandhya Arora,“ Recognition of Hand Written Devnagari Characters with Percentage Component Regular Expression Matching and Classification Tree”, IEEE, 2007.
  42. B.V.Dhandra, Mallikarjun Hangarge, “Global and Local Features Based Handwritten Text Words and Numerals Script Identification”, Intl. Conf. on Computational Intelligence and Multimedia Applications, PP 471-475. 2007.
  43. Naresh Kumar Garg, Lakhwinder Kaur, M. K. Jindal, “A New Method for Line Segmentation of Handwritten Hindi Text”, 7th International Conference on Information Technology, 2010.
  44. J. Hu, T. Pavlidis, “A Hierarchical Approach to Efficient Curvilinear Object Searching”, Computer Vision and Image Understanding, vol.63 (2), pp. 208-220, 1996.
  45. R. J. Ramteke, S. C. Mehrotra, “Recognition of Handwritten Devnagari Numerals”, International Journal of Computer Processing of Oriental Languages, 2008.
  46. Sandhya Arora et al., “Performance Comparison of SVM and ANN for Handwritten Devnagari Character Recognition”, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, May 2010.
  47. U. Bhattacharya, S. Vajda, A. Mallick, B. B. Chaudhuri, A. Belaid, “On the Choice of Training Set, Architecture and Combination Rule of Multiple MLP Classifiers for Multiresolution Recognition of Handwritten Characters”, 9th Int’l Workshop on Frontiers in Handwriting Recognition (IWFHR-9 2004).
  48. U. Pal and B. B. Chaudhuri, “Automatic Separation of Machine-Printed and Hand-Written Text Lines”.
  49. Anoop M. Namboodiri, Anil K. Jain,” Online Handwritten Script Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 1, January 2004.
  50. M. Hanmandlu, O.V., Ramana Murthy, Vamsi Krishna Madasu, “Fuzzy Model based Recognition of Handwritten Hindi Characters”, IEEE, 2007.
  51. P M Patil, T R Sontakke,” Rotation, scale and translation invariant handwritten Devanagari numeral character recognition using general fuzzy neural network”, Pattern Recognition, Elsevier , 2007.
  52. Judith Hochberg, Lila Kerns, Patrick Kelly, and Timothy Thomas, “Automatic Script Identification from Images using Cluster-based Templates”, IEEE transactions on PAMI, Vol.19, issue-2 pp 176 – 181, 1997.
  53. N. Sharma, U. Pal, F. Kimura, and S. Pal, “Recognition of Off-Line Handwritten Devnagari Characters Using Quadratic Classifier”, ICVGIP 2006, LNCS 4338, pp. 805 – 816,
  54. M. Egmont-Petersen, D. de Ridder, H. Handels, “Image Processing with Neural Networks: A Review”, Pattern Recognition, Vol 35, pp. 2279–2301, 2002.
  55. C. V. Jawahar, M. N. S. S. K. Pavan Kumar, S. S. Ravi Kiran, “A Bilingual OCR for Hindi-Telugu Documents and its Applications”, Seventh International Conference on Document Analysis and Recognition (ICDAR 2003).
  56. Umapada Pal, Sukalpa Chanda Tetsushi, Wakabayashi, Fumitaka Kimura, “Accuracy Improvement of Devnagari Character Recognition Combining SVM and MQDF”.
  57. K. Y. Rajput and Sangeeta Mishra, “Recognition and Editing of Devnagari Handwriting Using Neural Network”, SPIT-IEEE Colloquium and Intl. Conference, Mumbai, India.
  58. U. Bhattacharya, S. K. Parui , B. Shaw, K. Bhattacharya, “ Neural Combination of ANN and HMM for Handwritten Devnagari Numeral Recognition”.
  59. M N S S K Pavan Kumar, S S Ravikiran, Abhishek Nayani, C V Jawahar, P J Narayanan , “Tools for Developing OCRs for Indian Scripts”.
  60. Satish Kumar, “Evaluation of Orthogonal Directional Gradients on Hand-Printed Datasets”, Intl. Journal of Information Technology and Knowledge Management, Volume 2, No. 1, pp. 203-207. Jan - Jun 2009.
  61. Satish Kumar, “Performance and Comparison of Features on Devanagari Hand-printed Dataset”, Inlt. Journal of Recent Trends in Engineering, Vol. 1, No. 2, May 2009.
  62. Sandhya Arora, Debotosh Bhattacharjee, Mita Nasipuri, D. K. Basu, M. Kundu, “ Recognition of Non-Compound Handwritten Devnagari Characters using a Combination of MLP and Minimum Edit Distance”, International Journal of Computer Science and Security (IJCSS),Volume (4) : Issue-1 pp 107-120.
  63. Sung-Bae Cho, “Fusion of neural networks with fuzzy logic and genetic algorithm”, 2002 – IOS Press, pp 363–372.
  64. Prachi Mukherji, Priti P. Rege, “Shape Feature and Fuzzy Logic Based Offline Devnagari Handwritten Optical Character Recognition”, Journal of Pattern Recognition Research 4 (2009), pp 52-68.
  65. S. Kumar and C. Singh, “A Study of Zernike Moments and its use in Devnagari Handwritten Character Recognition”, Intl. Conf. on Cognition and Recognition, pp. 514-520, 2005.
  66. V. H. Mankar et al. “Contour Detection and Recovery through Bio-Medical Watermarking for Telediagnosis,” International Journal of Tomography & Statistics, Vol. 14 (Special Volume), Number S10, summer 2010.
  67. ISI Kolkata website http://www.isical.ac.in/~ujjwal/ download /database.html.
Index Terms

Computer Science
Information Sciences

Keywords

Devnagari Character Recognition Off-line Handwriting Recognition Segmentation Feature Extraction Image Classification