skip to main content
research-article

Application of Structural and Topological Features to Recognize Online Handwritten Bangla Characters

Published:13 February 2018Publication History
Skip Abstract Section

Abstract

This article presents a set of novel features for robust online Bangla handwritten character recognition. Two feature extraction methods are presented here. The first describes the transition from background to foreground pixels and vice versa. The second uses a combination of topological features and centre-of-gravity- (CG) based circular features where global information, local information, and Circular Quadrant Mass Distribution information have been extracted. The impact of each along with their combination have also been analyzed. A total of 15,000 isolated online Bangla character samples have been collected and used for the evaluation. A Support Vector Machine classifier records the best recognition rate when the transition count feature, CG-based circular features, and topological features are combined.

References

  1. S. Agarwal and V. Kumar. 2005. Online character recognition. In Proceedings of the 3rd International Conference on Information Technology and Applications. 698--703. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. B. B. Ahmed, S. Naz, M. I. Razzak, S. F. Rashid, M. Z. Afzal, and T. M. Breuel. 2016. Evaluation of cursive and non-cursive scripts using recurrent neural networks. Int. J. Neur. Comput. Appl. 27, 3 (2016), 603--613. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. F. Alvaro and R. Zanibbi. 2013. A shape-based layout descriptor for classifying spatial relationships in handwritten math. In Proceedings of the ACM Symposium on Document Engineering. 123--126. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. C. Bahlmann and H. Burkhardt. 2004. The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping. IEEE Trans. Pattern Anal. Mach. Intell. 26, 3 (2004), 299--310. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. A. Bandyopadhyay and B. Chakraborty. 2009. Development of online handwriting recognition system: A case study with handwritten bangla character. In Proceedings of the World Congress on Nature and Biologically Inspired Computing. 514--519.Google ScholarGoogle Scholar
  6. R. K. Bawa and R. Rani. 2011. A preprocessing technique for recognition of online handwritten gurmukhi numerals. In Proceedings of the International Conference on High Performance Architecture and Grid Computing. IEEE, 275--281.Google ScholarGoogle Scholar
  7. N. Bhattacharya, U. Pal, and F. Kimura. 2013. A system for bangla online handwritten text. In Proceedings of the International Conference on Document Analysis and Recognition. 1335--1339. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. U. Bhattacharya, R. Banerjee, S. Baral, R. Dey, and S. K. Parui. 2012. A semi automatic annotation scheme for Bangla online mixed cursive handwriting samples. In Proceedings of the International Conference on Frontiers in Handwriting Recognition. 680--685. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. U. Bhattacharya, B. K. Gupta, and S. K. Parui. 2007. Direction code based features for recognition of online handwritten characters of bangla. In Proceedings of the International Conference on Document Analysis and Recognition. 58--62. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. M. R. Bouguelia, S. Nowaczyk, and A. Verikas K. C. Santosh. 2017. Agreeing to disagree: Active learning with noisy labels without crowdsourcing. Int. J. Mach. Learn. Cybernet. (2017), 1--13.Google ScholarGoogle Scholar
  11. S. D. Connell, R. M. K. Sinha, and A. K. Jain. 2000. Recognition of unconstrained online devanagari characters. In Proceedings of the 15th International Conference on Pattern Recognition. 368--371.Google ScholarGoogle Scholar
  12. J. Demsar. 2006. Statistical comparisons of classifiers over multiple data sets. Int. J. Mach. Learn. Res. 7 (2006), 1--30. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. J. Du, J. Zhai, and J. Hu. 2017. Writer adaptation via deeply learned features for online Chinese handwriting recognition. Int. J. Doc. Anal. Recogn. 20, 1 (2017), 69--78. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. A. El-Sawy, H. EL-Bakry, and M. Loey. 2016. CNN for handwritten arabic digits recognition based on LeNet-5. In Proceedings of the International Conference on Advanced Intelligent Systems and Informatics. 566--575.Google ScholarGoogle Scholar
  15. M. Farha, G. Srinivasa, A. J. Ashwini, and K. Hemant. 2013. Online handwritten character recognition. Int. J. Comput. Sci. 11, 5 (2013), 30--36.Google ScholarGoogle Scholar
  16. G. A. Fink, S. Vajda, U. Bhattacharya, S. Parui, and B. B. Chaudhuri. 2010. Online bangla word recognition using sub-stroke level features and hidden markov models. In Proceedings of the International Conference on Frontiers in Handwriting Recognition. 393--398. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. V. Frinken, N. Bhattacharya, and U. Pal. 2014. Design of unsupervised feature extraction system for on-line handwriting recognition. In Proceedings of the 11th IAPR International Workshop on Document Analysis and Systems. 355--359. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. A. Fu, C. Dong, and L. Wang. 2015. An experimental study on stability and generalization of extreme learning machines. Int. J. Mach. Learn. Cybernet. 6, 1 (2015), 129--135.Google ScholarGoogle ScholarCross RefCross Ref
  19. R. Ghosh. 2015. A novel feature extraction approach for online bengali and devanagari character recognition. In Proceedings of the International Conference on Signal Processing and Integrated Networks. 483--488.Google ScholarGoogle ScholarCross RefCross Ref
  20. M. Gupta, N. Gupta, and R. Agrawal. 2012. Recognition of online handwritten gurmukhi strokes using support vector machine. In Proceedings of the International Conference on Bio-Inspired Computing: Theories and Application. 495--506.Google ScholarGoogle Scholar
  21. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. 2009. The WEKA data mining software: An update. ACM SIGKDD Explor. Newslett. 11, 1 (2009), 10--18. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. N. Joshi, G. Sita, A. G. Ramakrishnan, and V. Deepu. 2005. Machine recognition of online handwritten devanagari characters. In Proceedings of the International Conference on Document Analysis and Recognition. 1156--1160. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. S. Kubatur, M. Sid-Ahmed, and M. Ahmadi. 2012. A neural network approach to online Devanagari handwritten character recognition. In Proceedings of the International Conference on High Performance Computing and Simulation.Google ScholarGoogle Scholar
  24. A. Kumar and S. Bhattacharya. 2010. Online devanagari isolated character recognition for the iPhone using hidden markov models. In Proceedings of the International Conference on Students’ Technology Symposium. 300--304.Google ScholarGoogle Scholar
  25. V. L. Lajish and S. K. Kopparapu. 2014. Online handwritten Devanagari stroke recognition using extended directional features. In Proceedings of the 8th International Conference on Signal Processing and Communication System. IEEE.Google ScholarGoogle Scholar
  26. T. Mondal, U. Bhattacharya, S. K. Parui, K. Das, and D. Mandalapu. 2010. Online handwriting recognition of indian scripts - the first benchmark. In Proceedings of the 12th International Conference on Frontiers in Handwriting Recognition. 200--205. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. S. K. Parui, K. Guin, U. Bhattacharya, and B. B. Chaudhuri. 2008. Online handwritten bangla character recognition using HMM. In Proceedings of the International Conference on Pattern Recognition. 1--4.Google ScholarGoogle Scholar
  28. J. R. Prasad and U. Kulkarni. 2015. Gujarati character recognition using adaptive neuro fuzzy classifier with fuzzy hedges. Int. J. Mach. Learn. Cybernet. 6, 5 (2015), 763--775.Google ScholarGoogle ScholarCross RefCross Ref
  29. J. D. Prusa and T. M. Khoshgoftaar. 2017. Improving deep neural network design with new text data representations. Int. J. Big Data 4, 7 (2017).Google ScholarGoogle Scholar
  30. K. Roy. 2012. Stroke-database design for online handwriting recognition in bangla. Int. J. Mod. Eng. Res. 2, 4 (2012), 2534--2540.Google ScholarGoogle Scholar
  31. K. Roy, N. Sharma, and U. Pal. 2007. Online bangla handwriting recognition system. In Proceedings of the International Conference on Advances in Pattern Recognition. 117--122.Google ScholarGoogle Scholar
  32. M. K. Sachan, G. Lehal Singh, and V. K. Jain. 2011. A novel method to segment online gurmukhi script. In Proceedings of the International Conference on Information Systems for Indian Languages. 1--8.Google ScholarGoogle Scholar
  33. M. K. Sachan, G. Lehal Singh, and V. K. Jain. 2011. A system for online gurmukhi script recognition. In Proceedings of the International Conference on Information Systems for Indian Languages. 299--300.Google ScholarGoogle Scholar
  34. K. C. Santosh and J. Iwata. 2012. Stroke-Based Cursive Character Recognition. Xiaoqing Ding, INTECH.Google ScholarGoogle Scholar
  35. K. C. Santosh, C. Nattee, and B. Lamiroy. 2012. Relative positioning of stroke-based clustering: A new approach to online handwritten devanagari character recognition. Int. J. Image Graph. 12, 2 (2012), 25 pages.Google ScholarGoogle ScholarCross RefCross Ref
  36. K. C. Santosh and L. Wendlingc. 2015. Character recognition based on non-linear multi-projection profiles measure. Int. J. Front. Comput. Sci. 9, 5 (2015), 678--690. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. S. P. Sen, A. Bhattacharyya, A. Das, R. Sarkar, and K. Roy. 2016. Design of novel feature vector for recognition of online handwritten bangla basic characters. In Proceedings of the 1st International Conference on First International Conference on Intelligent Computing 8 Communication. 485--494.Google ScholarGoogle Scholar
  38. S. P. Sen, M. Mitra, S. Chowdhury, R. Sarkar, and K. Roy. 2016. Quad-tree based image segmentation and feature extraction to recognize online handwritten bangla characters. In Proceedings of the 7th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition. Ulm, Germany, 246--256.Google ScholarGoogle Scholar
  39. S. P. Sen, R. Sarkar, and K. Roy. 2016. A simple and effective technique for online handwritten bangla character recognition. In Proceedings of the 4th International Conference on Frontiers in Intelligent Computing: Theory and Application. 201--209.Google ScholarGoogle Scholar
  40. S. P. Sen, R. Sarkar, K. Roy, and N. Hori. 2016. Recognize online handwritten bangla characters using hausdorff distance based feature. In Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Application. 541--549.Google ScholarGoogle Scholar
  41. A. Sharma and K. Dahiya. 2012. Online handwriting recognition of gurmukhi and devanagiri characters in mobile phone devices. In Proceedings of the International Conference on Recent Advances and Future Trends in Information Technology. 37--41.Google ScholarGoogle Scholar
  42. A. Sharma, R. Kumar, and R. K. Sharma. 2008. Online handwritten gurmukhi character recognition using elastic matching. In Proceedings of the Congress on Image and Signal Processing. IEEE, 391--396. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. A. Sharma, R. Kumar, and R. K. Sharma. 2009. Rearrangement of recognized strokes in online handwritten gurmukhi words recognition. In Proceedings of the 10th International Conference on Document Analysis and Recognition. 1241--1245. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. A. Sharma, R. Kumar, and R. K. Sharma. 2010. HMM-based online handwritten gurmukhi character recognition. Int. J. Mach. Graph. Vision 19, 4 (2010), 439--449. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. P. K. Singh, R. Sarkar, N. Das, S. Basu, and M. Nasipuri. 2014. Statistical comparison of classifiers for script identification from multi-script handwritten documents. Int. J. Appl. Pattern Recogn. 1, 2 (2014), 152--172.Google ScholarGoogle ScholarCross RefCross Ref
  46. H. Swethalakshmi, A. Jayaraman, V. S. Chakravarthy, and C. C. Sekhar. 2006. Online handwritten character recognition for devanagari and telugu scripts using support vector machines. In Proceedings of the International Workshop on Frontiers in Handwriting Recognition. 367--372.Google ScholarGoogle Scholar
  47. H. Swethalakshmi, C. C. Sekhar, and V. S. Chakravarthy. 2007. Spatiostructural features for recognition of online handwritten characters in Devanagari and Tamil scripts. In Proceedings of the International Conference on Artificial Neural Networks. 230--239. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. X. Tang, Y. Ding, and K. Hao. 2017. A novel method based on line-segment visualizations for hyper-parameter optimization in deep networks. Int. J. Pattern Recogn. Artif. Intell. 32, 3 (2017), 1851002-1--1851002-15.Google ScholarGoogle ScholarCross RefCross Ref
  49. C. C. Tappert, C. Y. Suen, and T. Wakahara. 1990. The state of online handwriting recognition. In Transaction on Pattern Analysis and Machine Intelligence. IEEE 12, 8 (1990), 787--808. Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. A. Tripathi, S. S. Paul, and V. K. Pandey. 2012. Standardisation of stroke order for online isolated Devanagari character recognition for iPhone. In Proceedings of the International Conference on Technology Enhanced Education. IEEE, 1--5.Google ScholarGoogle Scholar
  51. Gerrit J. J. van den Burg and Patrick J. F. Groenen. 2016. GenSVM: A generalized multiclass support vector machine. J. Mach. Learn. Res. 17, 225 (2016), 1--42. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. G. D. Vescovo and A. Rizzi. 2007. Online handwriting recognition by the symbolic histograms approach. In Proceedings of the International Conference on Granular Computing. IEEE, 686--690. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. W. Wang, G.Tan, and H. Wang. 2017. Cross-domain comparison of algorithm performance in extracting aspect-based opinions from chinese online reviews. Int. J. Machine. Learn. Cybernet. 8, 3 (2017), 1053--1070.Google ScholarGoogle ScholarCross RefCross Ref
  54. Q. Wu, Z. Gui, S. Li, and J. Ou. 2017. Directly connected convolutional neural networks. Int. J. Pattern Recogn. Artif. Intell. 32, 5 (2017), 1859007-1--1859007-17.Google ScholarGoogle ScholarCross RefCross Ref
  55. M. F. Zafar, D. Mohammad, and M. M. Anwar. 2006. Recognition of online isolated handwritten characters by back propagation neural nets using sub-character primitive features. In Proceedings of the 9th International Conference on INMIC. IEEE, 157--162.Google ScholarGoogle Scholar
  56. W. Zhao, J. F. Liu, and X. L. Tang. 2002. Online handwritten english word recognition based on cascade connection of character HMMs. In Proceedings of the International Conference on Machine Learning and Cybernetics. IEEE, 1758--1761.Google ScholarGoogle Scholar

Index Terms

  1. Application of Structural and Topological Features to Recognize Online Handwritten Bangla Characters

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      Full Access

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader
      About Cookies On This Site

      We use cookies to ensure that we give you the best experience on our website.

      Learn more

      Got it!