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Handwritten Digit String Recognition for Indian Scripts

  • Hongjian ZhanEmail author
  • Pinaki Nath Chowdhury
  • Umapada Pal
  • Yue Lu
Conference paper
  • 75 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12047)

Abstract

In many documents digits/numerals may touch each other and hence digit string recognition is necessary as segmentation of individual numeral from the touching string is difficult. In this paper, we propose a digit string recognition system for four Indian popular scripts. Here we consider strings of Kannada, Oriya, Tamil and Telugu scripts for our experiment. This paper has two contributions: (i) we have developed 4 datasets of digit string for each of these four scripts. Each dataset has 20000 numeral string samples for training and 30000 samples for testing. As there is no such dataset available, it will be helpful to the community (ii) we apply a RNN free CNN (Convolutional Neural Network) and CTC (Connectionist Temporal Classifica-tion) based architecture for numeral string recognition. Unlike normal text string, in string of digits has no contextual information among the digits and hence a digit may be followed by an arbitrary digit in a digit string. Because of such behaviors we apply a CNN and CTC based architecture without RNN for numeral string recognition. We tested our scheme on our different test datasets and results are provided.

Keywords

String recognition Convolutional Neural Network Connectionist Temporal Classification Postal Automation 

References

  1. 1.
    Plamondon, R., Srihari, S.N.: On-line and off-line handwritten recognition: a comprehensive survey. IEEE Trans. PAMI 22, 62–84 (2000)CrossRefGoogle Scholar
  2. 2.
    Pal, U., Chaudhuri, B.: Indian script character recognition: a survey. Pattern Recogn. 37, 1887–1899 (2004)CrossRefGoogle Scholar
  3. 3.
    Bhowmick, T., et al.: An HMM based recognition scheme for handwritten Oriya numerals. In: Proceedings of the 9th International conference on Information Technology, pp. 105–110 (2006)Google Scholar
  4. 4.
    Sharma, N., Pal, U., Kimura, F.: Recognition of handwritten Kannada numerals. In: Proceedings of the 9th International Conference on Information Technology, pp. 133–136 (2006)Google Scholar
  5. 5.
    Hanmandlu, M., Ramana Murthy, O.: Fuzzy model based recognition of handwritten numerals. Pattern Recogn. 40, 1840–1854 (2007)CrossRefGoogle Scholar
  6. 6.
    Wen, Y., Lu, Y., Shi, P.: Handwritten Bangla numeral recognition system and its appli-cation to postal automation. Pattern Recogn. 40, 99–107 (2007)CrossRefGoogle Scholar
  7. 7.
    Bajaj, R., Dey, L., Chaudhury, S.: Devnagari numeral recognition by combining deci-sion of multiple connectionist classifiers. Sadhana 27, 59–72 (2002)CrossRefGoogle Scholar
  8. 8.
    Kumar, S., Singh, C.: A study of Zernike moments and its use in Devnagari handwrit-ten character recognition. In: Proceedings of the International conference on Cognition and Recognition, pp. 514–520 (2005)Google Scholar
  9. 9.
    Bhattacharya, U., et al.: Neural combination of ANN and HMM for handwritten Devnagari numeral recognition. In: Proceedings of the 10th International Workshop on Frontiers of Handwriting Recognition, pp. 613–618 (2006)Google Scholar
  10. 10.
    Otsu, N.: A Threshold selection method from grey level histogram. IEEE Trans. SMC 9, 62–66 (1979)Google Scholar
  11. 11.
    Kimura, F., et al.: Modified quadratic discriminant function and the application to Chinese character recognition. IEEE Trans. PAMI 9, 149–153 (1987)CrossRefGoogle Scholar
  12. 12.
    Huang, G., Liu, Z., Weinberger, K., Maaten, L.: Densely connected convolutional networks (2016). arXiv preprint arXiv:1608.06993
  13. 13.
    Graves, A., Fernndez, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of the 23rd International Conference on Machine learning, pp. 369–376 (2006)Google Scholar
  14. 14.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  15. 15.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of International Conference on Machine Learning, pp. 448–456 (2015)Google Scholar
  16. 16.
    Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks, In: Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, pp. 315–323 (2011)Google Scholar
  17. 17.
    Hinton, G., et al.: Improving neural networks by preventing co-adaptation of feature detectors (2012). arXiv preprint arXiv:1207.0580
  18. 18.
    Pal, U., Roy, K., Kimura, F.: Bangla handwritten pin code string recognition for indian postal automation. In: Proceedings of International Conference on Frontiers in Handwriting Recognition, pp. 290–295 (2008)Google Scholar
  19. 19.
    Pal, U., Roy, K., Kimura, F., Indian multi-script full pincode string recognition for postal automation, In: Proceedings of the 10th International Conference on Document Analysis and Recognition (ICDAR), pp. 456–460 (2009)Google Scholar
  20. 20.
    Jia, Y., et al.: Caffe: convolutional architecture for fast fea-ture embedding (2014). arXiv preprint arXiv:1408.5093

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Hongjian Zhan
    • 1
    Email author
  • Pinaki Nath Chowdhury
    • 2
  • Umapada Pal
    • 2
  • Yue Lu
    • 1
  1. 1.Shanghai Key Laboratory of Multidimensional Information Processing, Department of Computer Science and TechnologyEast China Normal UniversityShanghaiChina
  2. 2.CVPR UnitIndian Statistical InstituteKolkataIndia

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