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Handwriting Recognition in Indian Regional Scripts: A Survey of Offline Techniques

Published:01 March 2012Publication History
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Abstract

Offline handwriting recognition in Indian regional scripts is an interesting area of research as almost 460 million people in India use regional scripts. The nine major Indian regional scripts are Bangla (for Bengali and Assamese languages), Gujarati, Kannada, Malayalam, Oriya, Gurumukhi (for Punjabi language), Tamil, Telugu, and Nastaliq (for Urdu language). A state-of-the-art survey about the techniques available in the area of offline handwriting recognition (OHR) in Indian regional scripts will be of a great aid to the researchers in the subcontinent and hence a sincere attempt is made in this article to discuss the advancements reported in this regard during the last few decades. The survey is organized into different sections. A brief introduction is given initially about automatic recognition of handwriting and official regional scripts in India. The nine regional scripts are then categorized into four subgroups based on their similarity and evolution information. The first group contains Bangla, Oriya, Gujarati and Gurumukhi scripts. The second group contains Kannada and Telugu scripts and the third group contains Tamil and Malayalam scripts. The fourth group contains only Nastaliq script (Perso-Arabic script for Urdu), which is not an Indo-Aryan script. Various feature extraction and classification techniques associated with the offline handwriting recognition of the regional scripts are discussed in this survey. As it is important to identify the script before the recognition step, a section is dedicated to handwritten script identification techniques. A benchmarking database is very important for any pattern recognition related research. The details of the datasets available in different Indian regional scripts are also mentioned in the article. A separate section is dedicated to the observations made, future scope, and existing difficulties related to handwriting recognition in Indian regional scripts. We hope that this survey will serve as a compendium not only for researchers in India, but also for policymakers and practitioners in India. It will also help to accomplish a target of bringing the researchers working on different Indian scripts together. Looking at the recent developments in OHR of Indian regional scripts, this article will provide a better platform for future research activities.

References

  1. Ager, S. 2009. Omniglot - Writing systems and languages of the world. http://www.omniglot.com.Google ScholarGoogle Scholar
  2. Acharya, U. D, Reddy, N. V. S., and Krishnamoorthi, M. 2008. Multilevel classifiers in the recognition of handwritten Kannada numerals. In Proceedings of World Academy of Science, Engineering and Technology (WASET’08). 42, 278--283.Google ScholarGoogle Scholar
  3. Alireza, A., Pal. U., and Nagabhushan, P. 2011. A new scheme for unconstrained handwritten text-line segmentation. Patt. Recog. 44, 4, 917--928. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Aradhya, V. N. M., Kumar, G. H., and Noushath, S. 2007. Robust unconstrained handwritten digit recognition using radon transform. In Proceedings of the International Consortium of Stem Cell Networks (ICSCN’07). 626--629.Google ScholarGoogle Scholar
  5. Aradhya, V. N. M, Niranjan, S. K., and Kumar, G. H. 2010. Probabilistic neural network based approach for handwritten character recognition. Int. J. Comp. Comput. Technol. (Special Issue) 1, 9--13.Google ScholarGoogle Scholar
  6. Basu, S., Chaudhuri, C., Kundu, M., Nasipuri, M., and Basu, D. K. 2007a. Text line extraction from multi-skewed handwritten documents. Patt. Recog. 40, 6, 1825--1839. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Basu, S., Das, N., Sarkar, R., Kundu, M., Nasipuri, M., and Basu, D. K. 2007b. A fuzzy technique for segmentation of handwritten Bangla word images. In Proceedings of the International Conference on Computer Theory and Applications (ICCTA’07). 427--433. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Basu, S., Das, N., Sarkar, R., Kundu, M., Nasipuri, M., and Basu, D. K. 2009. A hierarchical approach to recognition of handwritten Bangla characters. Patt. Recog. 42, 7, 1467--1484. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Basu, S., Das, N., Sarkar, R., Kundu, M., Nasipuri, M., and Basu, D. K. 2010. A novel framework for automatic sorting of postal documents with multi-script address blocks. Patt. Recog. 43, 10, 3507--3521. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Bhattacharya, U., Das, T. K., Datta, A., Parui, S. K., and Chaudhuri, B. B. 2002. A hybrid scheme for hand-printed numeral recognition based on self-organizing network and MLP classifiers. Int. J. Patt. Recog. Artifi. Intell. 16, 7, 845--864.Google ScholarGoogle ScholarCross RefCross Ref
  11. Bhattacharya, U. and Chaudhuri, B. B. 2003. A majority voting scheme for multi-resolution recognition of handprinted numerals. In Proceedings of the 7th International Conference on Document Analysis and Recognition (ICDAR’03). 16--20. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Bhattacharya, U., Vajda, S., Mallick, A., Chaudhuri. B. B., and Belaid, A. 2004. On the choice of training set, architecture and combination rule of multiple MLP classifiers for multi-resolution recognition of handwritten characters. In Proceedings of 9th International Workshop on the Frontiers of Handwriting Recognition (IWFHR’04). 419--424. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Bhattacharya, U., Shridhar, M., and Parui, S. K. 2006. On recognition of handwritten Bangla characters, In Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP’06). 817--828. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Bhattacharya, U., Ghosh, S. K., and Parui, S. K. 2007. A two-stage recognition scheme for handwritten Tamil characters. In Proceedings of the 9th International Conference on Document Analysis and Recognition (ICDAR’07). 511--515. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Bhattacharyya, K., and Sarma, K. K. 2009. ANN-based innovative segmentation method for handwritten text in Assamese. Int. J. Comput. Sci. Infor. 5, 9--16.Google ScholarGoogle Scholar
  16. Bhowmik, T. K., Bhattacharya, U., and Parui, S. K. 2004. Recognition of Bangla handwritten characters using an MLP classifier based on stroke features. In Proceedings of the International Conference on Neural Information Processing (ICONIP’04). 814--819.Google ScholarGoogle Scholar
  17. Bhowmik, T. K., Roy, A., and Roy, U. 2005. Character segmentation for handwritten Bangla words using artificial neural network. In Proceedings of IWNNLDAR’05. 28--32.Google ScholarGoogle Scholar
  18. Bhowmik, T. K., Parui, S. K., Bhattacharya, U., and Shaw, B. 2006. An HMM based recognition scheme for handwritten Oriya numerals. In Proceedings of the 9th Information Technology Conference (ICIT’06). 105--110. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Bhowmik, T. K., Parui, S. K., and Roy, U. 2008. Discriminative HMM Training with GA for Handwritten Word Recognition. In Proceedings of the 19th International Conference on Pattern Recognition (ICPR’08). 1--4.Google ScholarGoogle Scholar
  20. Bhowmik, T. K., Ghanty, P., Roy, A., and Parui, S. K. 2009. SVM-based hierarchical architectures for handwritten Bangla character recognition. Int. J. Document Analy. Recog. 12, 2, 97--108. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Bishnu, A. and Chaudhuri, B. B. 1999. Segmentation of Bangla handwritten text into characters by recursive contour following. In Proceedings of the 5th International Conference on Document Analysis and Recognition (ICDAR’99). 402--405. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Chaudhuri, B. B. and Majumdar, A. 2007. Curvelet-based multi SVM recognizer for offline handwritten bangla: A major Indian script. In Proceedings of the 9th International Conference on Document Analysis and Recognition (ICDAR’07). 491--495. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Chaudhuri, B. B and Bera, S. 2009. Handwritten text line identification in Indian scripts. In Proceedings of the International Conference on Document Analysis and Recognition (ICDAR’09). 636--640. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Chacko, B. P. and Anto, B. P. 2009. Discrete curve evolution based skeleton pruning for character recognition. In Proceedings of the 7th International Conference on Advancements in Pattern Recognition (ICAPR’09). 402--405. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Chacko, B. P. and Anto, B. P. 2010. Pre and post processing approaches in edge detection for character recognition. In Proceedings of the 12th International Conference on the Frontiers of Handwriting Recognitions (ICFHR’10). 676--681. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Chinnuswamy, P. and Krishnamoorthy, S. G. 1980. Recognition of hand printed Tamil characters. Patt. Recog. 12, 141--152.Google ScholarGoogle ScholarCross RefCross Ref
  27. Das, D. and Yasmin, R. 2006. Segmentation and recognition of unconstraint Bangla handwritten numerals. Asian J. Inf. Technol. 5, 2, 155--159.Google ScholarGoogle Scholar
  28. Das, N., Das, B., Sarkar, R., Basu, S., Kundu, M., and Nasipuri, M. 2010. Handwritten Bangla basic and compound character recognition using MLP and SVM classifier. J. Comput. 2, 2, 109--115.Google ScholarGoogle Scholar
  29. Desai, A. A. 2010. Gujarati handwritten numeral optical character reorganization through neural network. Patt. Recog. 43, 7, 2582--2589. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Dhandra, B. V., Benne, R. G., and Hangarge, M. 2007a. Handwritten Kannada numeral recognition based on structural features. In Proceedings of ICCIMA’07. 224--228. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Dhandra, B. V. and Hangarge, M. 2007b. Morphological reconstruction for word level script identification. Int. J. Comput. Sci. Secur. 1, 1, 41--51.Google ScholarGoogle Scholar
  32. Garg, N. 2009. Handwritten Gurumukhi character recognition using neural networks. Master’s thesis. Thapar University, Patiala.Google ScholarGoogle Scholar
  33. Gandhi, R. I. and Iyakutti, K. 2009. An attempt to recognize handwritten Tamil character using Kohonen SOM. Int. J. Advanced Network. Appl. 1, 3, 188--192.Google ScholarGoogle Scholar
  34. Ghosh, D., Dube, T., and Shivaprasad, A. P. 2010. Script recognition---A review. IEEE Trans. Patt. Analy. Machine Learn. 32, 12, 2142--2161. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Gowda, K. C., Vikram, T. N., and Urs, S. R. 2007. 2 directional 2 dimensional pairwise FLD for handwritten Kannada numeral recognition. In Proceedings of the International Conference on Asian Digital Libraries (ICADL’07). 499--501. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Guru, D. S., Ahmed, S. K., and Irfan, K. 2001. An attempt towards recognition of handwritten Urdu characters: A decision tree approach. In Proceedings of the National Conference on Computers and Information Technology (NCCIT’01). 75--83.Google ScholarGoogle Scholar
  37. Hangarge, M. and Dhandra, B. V. 2010. Offline handwritten script identification in document images. Int. J. Comput. Appl. 4, 6, 6--10.Google ScholarGoogle Scholar
  38. Haider, T. and Yusuf, M. 2007. Accelerated recognition of handwritten Urdu digits using shape context based gradual pruning. In Proceedings of the International Conference on Intelligent and Advanced Systems (ICIAS’07). 601--604.Google ScholarGoogle Scholar
  39. Hewavitharana, S. and Fernando, H. C. 2002. A two-stage classification approach to Tamil handwriting recognition. In Proceedings of the Tamil Internet Conference. 118--124.Google ScholarGoogle Scholar
  40. Hiremath, P. S., Shivashankar, S., Pujari, J. D., and Mouneswara, V. 2010. Script identification in a handwritten document image using texture features. In Proceedings of IADCC’10. 110--114.Google ScholarGoogle Scholar
  41. Hoque, M. M., Karim, M. R., Hossain, M. G., Arefin, M. S., and Hasan, M. M. 2008. Bangla numeral recognition engine. In Proceedings of the 5th International Conference on Electrical and Control Engineering (ICECE’08). 644--647.Google ScholarGoogle Scholar
  42. Islam, M. B., Azadi, M. M. B., Rahman, M. A., and Hashem, M. M. A. 2005. Bengali handwritten character recognition using modified syntactic method. In Proceedings of the 2nd National Conference on Computer Processing of Bangla (NCCPB’05). 264--275.Google ScholarGoogle Scholar
  43. Jayadevan, R., Kolhe, S. R., Patil, P. M., and Pal, U. 2011. Offline recognition of Devanagari script: A survey. IEEE Trans. on SMC-Part C: Appli. Rev. 41, 6, 782--796. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. John, R., Raju, G., and Guru, D. S. 2007. 1D wavelet transform of projection profiles for isolated handwritten Malayalam character recognition. In Proceedings of ICCIMA’07. 481--485. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Kannan, J. R., Prabhakar, R. and Suresh, R. M. 2008. Off-line cursive handwritten Tamil character recognition. In Proceedings of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Conference (ICST’08). 159--164. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Kannan, J. R. and Prabhakar, R. 2009. A comparative study of optical character recognition for Tamil script. Euro. J. Scientific Res. 35, 4, 570--582.Google ScholarGoogle Scholar
  47. Kumar, R. and Singh, A. 2010. Detection and segmentation of lines and words in Gurmukhi handwritten text. In Proceedings of the 2nd IACC’10. 353--356.Google ScholarGoogle Scholar
  48. Kunte, R. S. and Samuel R. D. S. 2006. Script independent handwritten numeral recognition. In Proceedings of the International Conference on Visual Information Engineering (VIE’06). 94--98.Google ScholarGoogle Scholar
  49. Lajish, V. L., Annapurneswari, C. K, and Narayanan, N. K. 2005. Recognition of handwritten word images using lexicon based word modeling and A* algorithm. In Proceedings of the International Conference on Cognition and Recognition (ICCR’05). 581--588.Google ScholarGoogle Scholar
  50. Lajish, V. L. 2007. Handwritten character recognition using perceptual fuzzy-zoning and class modular neural networks. In Proceedings of 4th the International Conference on Intelligent Information Technology (ICIIT’07). 188--192.Google ScholarGoogle ScholarCross RefCross Ref
  51. Lajish, V. L. 2008. Handwritten character recognition using gray-scale based state-space parameters and class modular NN. In Proceedings of the International Consortium of Stem Cell Networks (ICSCN’08). 374--379.Google ScholarGoogle ScholarCross RefCross Ref
  52. Liu, C. L. and Suen, C. Y. 2009. A new benchmark on the recognition of handwritten Bangla and Farsi numeral characters. Patt. Recog. 42, 12, 3287--3295. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Lu, S., Tu, X., and Lu, Y. 2008. An improved two-layer SOM classifier for handwritten numeral recognition. In Proceedings of the International Conference on Intelligent Information Technology (ICIIT’08). 367--371.Google ScholarGoogle Scholar
  54. Mashiyat, A. S., Mehadi, A. S., and Talukder, K. H. 2004. Bangla off-line handwritten character recognition using superimposed matrices. In Proceedings of the 7th International Conference on Intelligent Information Technology (ICIIT’04). 610--614.Google ScholarGoogle Scholar
  55. Moni, B. S. and Raju, G. 2011. Modified quadratic classifier and directional features for handwritten Malayalam character recognition. Int. J. Comput. Appl. (Special Issue on Computational Science), 30--34.Google ScholarGoogle Scholar
  56. Mukhtar, O., Setlur, S., and Govindaraju, V. 2009. Experiments on Urdu text recognition. In Guide to OCR for Indic Scripts, V. Govindaraju and S. Setlur Eds., 163--171.Google ScholarGoogle Scholar
  57. Nagabhushan, P. and Pai, R. M. 1999. Modified region decomposition method and optimal depth decision tree in the recognition of non-uniform sized characters - An experimentation with Kannada characters. Patt. Recog. Lett. 20, 1467--1475.Google ScholarGoogle ScholarCross RefCross Ref
  58. Nagabhushan, P., Angadi, S. A., and Anami, B. S. 2003. A fuzzy statistical approach to Kannada vowel recognition based on invariant moments. In Proceedings of the 2nd National Conference on Document Analysis and Recognition (NCDAR’03). 275--285.Google ScholarGoogle Scholar
  59. Niranjan, S. K., Kumar, V., Kumar, H. G., and Aradhya, V. N. M. 2008. FLD based unconstrained handwritten Kannada character recognition. In Proceedings of the International Conference on Future Generation Communication and Networking (ICFGCNS’08). 7--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. Niranjan, S. K., Kumar, V., Kumar, G. H., and Aradhya, V. N. M. 2009. FLD based unconstrained handwritten Kannada character recognition. J. Datab. Theory Appl. , 2, 3, 21--26.Google ScholarGoogle Scholar
  61. Pal, U. and Datta, S. 2003. Segmentation of Bangla unconstrained handwritten text. In Proceedings of the 7th International Conference on Document Analysis and Recognition (ICDAR’03). 1128--1132. Google ScholarGoogle ScholarDigital LibraryDigital Library
  62. Pal, U. and Sarkar, A. 2003. Recognition of printed Urdu Script. In Proceedings of the 7th International Conference on Document Analysis and Recognition (ICDAR’03). 1183--1187. Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. Pal, U. and Chaudhuri, B. B. 2004a. Indian script character recognition: A survey. Patt. Recog. 37, 9, 1887--1899.Google ScholarGoogle ScholarCross RefCross Ref
  64. Pal, U., Kundu, S., Ali, Y., Islam, H. and Tripathy, N. 2004b. Recognition of unconstrained Malayalam handwritten numeral. In Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP’04). 423--428.Google ScholarGoogle Scholar
  65. Pal U., Chaudhuri, B. B., and Belaid, A. 2006. A system for Bangla handwritten numeral recognition. IETE J. Res. 52, 1, 27--34.Google ScholarGoogle ScholarCross RefCross Ref
  66. Pal, U., Wakabayashi, T., and Kimura, F. 2007a. Handwritten Bangla compound character recognition using gradient feature. In Proceedings of the 10th Information Technology Conference (ICIT’07). 208--213. Google ScholarGoogle ScholarDigital LibraryDigital Library
  67. Pal, U., Wakabayashi, T., and Kimura, F. 2007b. A system for off-line Oriya handwritten character recognition using curvature feature. In Proceedings of the 10th Information Technology Conference (ICIT’07). 227--229. Google ScholarGoogle ScholarDigital LibraryDigital Library
  68. Pal, U., Wakabayashi, T., Sharma, N., and Kimura, F. 2007c. Handwritten Numeral Recognition of Six Popular Indian Scripts. In Proceedings of 9th International Conference on Document Analysis and Recognition (ICDAR’07). 749--753. Google ScholarGoogle ScholarDigital LibraryDigital Library
  69. Pal, U., Sharma, N., Wakabayashi, T., and Kimura, F. 2008. Handwritten character recognition of popular south Indian scripts. Lecture Notes in Computer Science, vol. 4768, D. Doermann and S. Jaeger, Eds., Springer Verlag, 251--264. Google ScholarGoogle ScholarDigital LibraryDigital Library
  70. Pal, U., Roy, K., and Kimura, F. 2009. A lexicon-driven handwritten city-name recognition scheme for Indian postal automation. IEICE Trans. Inf. Syst. E92.D, 5, 1146--1158.Google ScholarGoogle ScholarCross RefCross Ref
  71. Paulpandian, T. and Ganapathy, V. 1993. Translation and scale invariant recognition of handwritten Tamil characters using a hierarchical neural network. In Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS’93). 2439--2441.Google ScholarGoogle Scholar
  72. Plamondon, R. and Srihari, S. N. 2000. On-line and off-line handwritten recognition: A comprehensive survey. IEEE Trans. Patt. Analy. Machine Learn. 22, 1, 62--84. Google ScholarGoogle ScholarDigital LibraryDigital Library
  73. Prasad, J. R., Kulkarni, U. V., and Prasad, R. S. 2009. Template matching algorithm for Gujarati character recognition. In Proceedings of the 2nd International Conference on Emerging Trends in Engineering & Technology (ICETET’’09). 263--268. Google ScholarGoogle ScholarDigital LibraryDigital Library
  74. Purkait, P. and Chanda, B. 2010. Off-line recognition of handwritten Bengali numerals using morphological features. In Proceedings of the 12th International Conference on the Frontiers of Handwriting Recognition (ICFHR’10). 363--368. Google ScholarGoogle ScholarDigital LibraryDigital Library
  75. Ragha, L. R. and Sasikumar, M. 2010. Adapting moments for handwritten Kannada Kagunita recognition. In Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC’10). 125--129. Google ScholarGoogle ScholarDigital LibraryDigital Library
  76. Rahiman, M. A., Shajan, A., Elizabeth, A., Divya, M. K., Kumar, G. M., and Rajasree, M. S. 2010. Isolated handwritten Malayalam character recognition using HLH intensity patterns. In Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC’10). 147--151. Google ScholarGoogle ScholarDigital LibraryDigital Library
  77. Rahman, A. F. R., Rahman, R., and Fairhurst, M. C. 2002. Recognition of handwritten Bengali characters: A novel multistage approach. Patt. Recog. 35, 5, 997--1006.Google ScholarGoogle ScholarCross RefCross Ref
  78. Rajashekararadhya, S. V., Ranjan, V. P., and Aradhya, V. N. M. 2008a. Isolated handwritten Kannada and Tamil numeral recognition: A novel approach. In Proceedings of the International Conference on Emerging Trends in Engineering & Technology (ICETET’’08). 1192--1195. Google ScholarGoogle ScholarDigital LibraryDigital Library
  79. Rajashekararadhya, S. V. and Ranjan, P. V. 2008b. Neural network based handwritten numeral recognition of Kannada and Telugu scripts. In Proceedings of the IEEE Technology, Education, & Networking Conference (TENCon’’08). 1--5.Google ScholarGoogle Scholar
  80. Rajashekararadhya, S. V. and Ranjan, V. P. 2009a. Support vector machine based handwritten numeral recognition of Kannada script. In Proceedings of IACC’09. 381--386.Google ScholarGoogle Scholar
  81. Rajashekararadhya, S. V. and Ranjan, V. P. 2009b. Zone-based hybrid feature extraction algorithm for handwritten numeral recognition of four Indian scripts. In Proceedings of the International Conference on Systems, Man, and Cybernetics (ICSMC’09). 5145--5150.Google ScholarGoogle Scholar
  82. Rajiv, K. S. and Amardeep, S. D. 2010. Challenges in segmentation of text in handwritten Gurmukhi script. In Proceedings of ICRTBAIP’10. 388--392.Google ScholarGoogle Scholar
  83. Rajput, G. G. and Hangarge, M. 2007. Recognition of isolated handwritten Kannada numerals based on image fusion method. In Proceedings of ICPReMI’07. 153--160. Google ScholarGoogle ScholarDigital LibraryDigital Library
  84. Rajput, G. G. 2008. Unconstrained Kannada handwriten numeral recognition based upon image reduction and KNN classifier. In Proceedings of the International Conference on Cognition and Recognition (ICCR’08). 11--16.Google ScholarGoogle Scholar
  85. Rajput, G. G., Horakeri, R., and Sidramappa, C. 2010a. Printed and handwritten mixed Kannada numerals recognition using SVM. Int. J. Comput. Sci. Engin. 2, 5, 1622--1626.Google ScholarGoogle Scholar
  86. Rajput, G. G. and Anita, H. B. 2010b. Handwritten script recognition using DCT and wavelet features at block level. Int. J. Comput. Appl. (Special Issue on RTIPPR). 158--163.Google ScholarGoogle Scholar
  87. Raju, G. 2008. Wavelet transform and projection profiles in handwritten character recognition -- A performance analysis. In Proceedings of ICADCOM’08. 309--314.Google ScholarGoogle ScholarCross RefCross Ref
  88. Reddy, N. V. S and Nagabhushan, P. 1998a. A connectionist expert system model for conflict resolution in unconstrained handwritten numeral recognition. Patt. Recog. Lett. 19, 161--169. Google ScholarGoogle ScholarDigital LibraryDigital Library
  89. Reddy, N. V. S. and Nagabhushan, P. 1998b. A three-dimensional neural network model for unconstrained handwritten numeral recognition: A new approach. Patt. Recog. 31, 5, 511--516.Google ScholarGoogle ScholarCross RefCross Ref
  90. Roy, A., Bhowmik, T. K., Parui, S. K., and Roy, U. 2005. A novel approach to skew detection and character segmentation for handwritten Bangla words. In Proceedings of the Conference on Digital Image Computing: Techniques and Applications (DICTA’05). 30--37. Google ScholarGoogle ScholarDigital LibraryDigital Library
  91. Roy, K., Vajda S., Pal, U., and Chaudhuri, B. B. 2004a. A system towards Indian postal automation. In Proceedings of the 9th International Conference on the Frontiers of Handwriting Recognition (ICFHR’04). 580--585. Google ScholarGoogle ScholarDigital LibraryDigital Library
  92. Roy, K., Pal, U., and Chaudhuri, B. B. 2004b. A system for joining and recognition of broken Bangla numerals for Indian postal automation, In Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP’04). 581--586.Google ScholarGoogle Scholar
  93. Roy, K., Banerjee, A., and Pal, U. 2004c. A system for word-wise handwritten script identification for Indian postal automation. In Proceedings of INDICON’04. 266--271.Google ScholarGoogle Scholar
  94. Roy, K., Chaudhuri, C., Pal, U., and Kundu, M. 2005a. A Study on the Effect of Varying Training set Sizes on Recognition Performance with Handwritten Bangla Numerals. In Proceedings of INDICON’05. 570--574.Google ScholarGoogle Scholar
  95. Roy, K., Pal, U., and Kimura, F. 2005b. Bangla handwritten character recognition. In Proceedings of the 2nd International Joint Conference on Artificial Intelligence (IJCAI’05). 431--443.Google ScholarGoogle Scholar
  96. Roy, K., Pal, T., Pal, U., and Kimura, F. 2005c. Oriya Handwritten Numeral Recognition System. In Proceedings of the International Conference on Document Analysis and Recognition (ICDAR’05). 770--774. Google ScholarGoogle ScholarDigital LibraryDigital Library
  97. Roy, K. and Pal, U. 2006. Word-wise Hand-written Script Separation for Indian Postal automation. In Proceedings of 10th International Conference on the Frontiers of Handwriting Recognition (ICFHR’06). 521--526.Google ScholarGoogle Scholar
  98. Roy, K. 2008. On the development of an optical character recognition system for Indian postal automation. Ph.D. thesis. Jadavpur University, India.Google ScholarGoogle Scholar
  99. Roy, K. and Majumder, K. 2008. Trilingual script separation of handwritten postal document. In Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP’08). 693--700. Google ScholarGoogle ScholarDigital LibraryDigital Library
  100. Sagheer, M. W., He, C. L., Nobile, N., and Suen, C. Y. 2009. A new large Urdu database for off-line handwriting recognition. In Proceedings of the International Conference on Image Analysis and Processing (ICIAP’09). 538--546. Google ScholarGoogle ScholarDigital LibraryDigital Library
  101. Sagheer, M. W., He, C. L., Nobile, N., and Suen, C. Y. 2010. Holistic Urdu handwritten word recognition using support vector machine. In Proceedings of the 20th International Conference on Pattern Recognition (ICPR’10). 1900--1903. Google ScholarGoogle ScholarDigital LibraryDigital Library
  102. Sangame, S. K., Ramteke, R. J., and Benne, R. 2009. Recognition of isolated handwritten Kannada vowels. Adv. Computat. Res. 1, 2, 52--55.Google ScholarGoogle Scholar
  103. Sarkar, R., Das, N., Basu, S., Kundu, M., Nasipuri, M., and Basu, D. K. 2010. Word level script identification from Bangla and Devanagri handwritten texts mixed with Roman script. J. Comput. 2, 2, 103--108.Google ScholarGoogle Scholar
  104. Sarkar, A., Biswas, A., Bhowmick, P., and Bhattacharya, B. B. 2010b. Word segmentation and baseline detection in handwritten documents using isothetic covers. In Proceedings of the 12th International Conference on the Frontiers of Handwriting Recognition (ICFHR’10). 445--450. Google ScholarGoogle ScholarDigital LibraryDigital Library
  105. Sarma, K. K. 2009. Bi-lingual handwritten character and numeral recognition using multi-dimensional recurrent neural networks. Int. J. Electron. Electrical Engin. 3, 7, 443--450.Google ScholarGoogle Scholar
  106. Sastry, P. N., Krishnan, R., and Ram, B. V. S. 2010. Classification and identification of Telugu handwritten characters extracted from palm leaves using decision tree approach. J. Applied Engn. Sci. 5, 3, 22--32.Google ScholarGoogle Scholar
  107. Shanthi, N. and Duraiswamy, K. 2010. A novel SVM-based handwritten Tamil character recognition system. Patt. Anal. Applicat. 13, 2, 173--180.Google ScholarGoogle ScholarDigital LibraryDigital Library
  108. Sharma, D. V. and Lehal, G. S. 2006. An iterative algorithm for segmentation of isolated handwritten words in Gurmukhi script. In Proceedings of the 18th International Conference on Pattern Recognition (ICPR’06). 1022--1025. Google ScholarGoogle ScholarDigital LibraryDigital Library
  109. Sharma, D. and Jhajj, P. 2010. Recognition of isolated handwritten characters in Gurmukhi script. Int. J. Comput. Appl. 4, 8, 9--17.Google ScholarGoogle ScholarCross RefCross Ref
  110. Sharma, N., Pal, U., and Kimura, F. 2006. Recognition of handwritten Kannada numerals. In Proceedings of the 9th Information Technology Conference (ICIT’06). 133--136. Google ScholarGoogle ScholarDigital LibraryDigital Library
  111. Sitamahalakshmi, T., Babu, V., and Jagadeesh, M. 2010. Character recognition using Dempster-Shafer theory combining different distance measurement methods. Int. J. Engin. Sci. Technol. 2, 5, 1177--1184.Google ScholarGoogle Scholar
  112. Sukhaswami M. B., Seetharamulu, P., and Pujari, A. K. 1995. Recognition of Telugu characters using neural networks. Int. J. Neural Syst. 6, 3, 317--357.Google ScholarGoogle ScholarCross RefCross Ref
  113. Suresh, R. M., Arumugam, S., and Ganesan, L. 1999. Fuzzy approach to recognize handwritten Tamil characters. In Proceedings of ICCIMA’99. 459--463. Google ScholarGoogle ScholarDigital LibraryDigital Library
  114. Sutha, J. and Ramaraj, N. 2007. Neural network based offline Tamil handwritten character recognition system. In Proceedings of ICCIMA’07. 446--450. Google ScholarGoogle ScholarDigital LibraryDigital Library
  115. Tripathy, N. and Pal, U. 2004. Handwriting segmentation of unconstrained Oriya text. In Proceedings of 9th International Conference on the Frontiers of Handwriting Recognition (ICFHR’04). 306--311. Google ScholarGoogle ScholarDigital LibraryDigital Library
  116. Vajda, S. and Belaid, A. 2005. Structural information implant in a context based segmentation-free HMM handwritten word recognition system for latin and Bangla script. In Proceedings of 8th International Conference on Document Analysis and Recognition (ICDAR’05). 1126--1130. Google ScholarGoogle ScholarDigital LibraryDigital Library
  117. Vajda, S., Roy, K., Pal, U., Chaudhuri, B. B., and Belaid, A. 2009. Automation of Indian postal documents written in Bangla and English. Int. J. Patt. Recog. Artif. Intell. 23, 8, 1599--1632.Google ScholarGoogle ScholarCross RefCross Ref
  118. Vikram, T. N., Gowda, K. C., and Urs, S. R. 2008. Symbolic representation of Kannada characters for recognition. In Proceedings of the International Conference on Networking, Sensing, and Control (ICNSC’08). 823--826.Google ScholarGoogle Scholar
  119. Xu, J. W., Xu, J., and Lu, Y. 2008. Handwritten Bangla digit recognition using hierarchical Bayesian network. In Proceedings of ICISKE’08. 1096--1099.Google ScholarGoogle Scholar
  120. Yusuf, M. and Haider, T. 2004. Recognition of handwritten Urdu digits using shape context. In Proceedings of the 8th IEEE International Multi-Topic Conference (INMIC’04). 569--572.Google ScholarGoogle Scholar
  121. Wen, Y., Lub, Y., and Shi, P. 2007. Handwritten Bangla numeral recognition system and its application to postal automation. Patt. Recog. 40, 1, 99--107. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Handwriting Recognition in Indian Regional Scripts: A Survey of Offline Techniques

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              cover image ACM Transactions on Asian Language Information Processing
              ACM Transactions on Asian Language Information Processing  Volume 11, Issue 1
              March 2012
              72 pages
              ISSN:1530-0226
              EISSN:1558-3430
              DOI:10.1145/2090176
              Issue’s Table of Contents

              Copyright © 2012 ACM

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              Publication History

              • Published: 1 March 2012
              • Accepted: 1 May 2011
              • Revised: 1 March 2011
              • Received: 1 December 2010
              Published in talip Volume 11, Issue 1

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