Improved BLSTM Neural Networks for Recognition of On-Line Bangla Complex Words

  • Volkmar Frinken
  • Nilanjana Bhattacharya
  • Seiichi Uchida
  • Umapada Pal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8621)


While bi-directional long short-term (BLSTM) neural network have been demonstrated to perform very well for English or Arabic, the huge number of different output classes (characters) encountered in many Asian fonts, poses a severe challenge. In this work we investigate different encoding schemes of Bangla compound characters and compare the recognition accuracies. We propose to model complex characters not as unique symbols, which are represented by individual nodes in the output layer. Instead, we exploit the property of long-distance-dependent classification in BLSTM neural networks. We classify only basic strokes and use special nodes which react to semantic changes in the writing, i.e., distinguishing inter-character spaces from intra-character spaces. We show that our approach outperforms the common approaches to BLSTM neural network-based handwriting recognition considerably.


Handwritten Text BLSTM NN Bangla Text Complex Temporal Pattern 


  1. 1.
    Bhattacharya, N., Pal, U., Kimura, F.: A System for Bangla Online Handwritten Text. In: 12th Int’l Conf. on Document Analysis and Recognition, pp. 1367–1371 (2013)Google Scholar
  2. 2.
    Bhattacharya, U., Guin, K., Parui, S.K.: Direction Code Based Features for Recognition of Online Handwritten Characters of Bangla. In: 9th Int’l Conf. on Document Analysis and Recognition, vol. 1, pp. 58–62 (2007)Google Scholar
  3. 3.
    Bhattacharya, U., Nigam, A., Rawat, Y.S., Guin, K.: An Analytic Scheme for Online Handwritten Bangla Cursive Word Recognition. In: 11th Int’l Conf. Frontiers in Handwriting Recognition, pp. 320–325 (2008)Google Scholar
  4. 4.
    Fink, G., Vajda, S., Bhattacharya, U., Parui, S.K., Chaudhuri, B.B.: Online Bangla Word Recognition Using Sub-Stroke Level Features and Hidden Markov Models. In: Int’l Conf. of Frontiers in Handwriting Recognition, pp. 393–398 (2010)Google Scholar
  5. 5.
    Frinken, V., Bhattacharya, N., Pal, U.: Design of Unsupervised Feature Extraction System for On-Line Bangla Handwriting Recognition. In: 11th IAPR International Workshop on Document Analysis Systems (page accepted for publication, 2014)Google Scholar
  6. 6.
    Frinken, V., Peter, T., Fischer, A., Bunke, H., Do, T.-M.-T., Artieres, T.: Improved Handwriting Recognition by Combining Two Forms of Hidden Markov Models and a Recurrent Neural Network. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 189–196. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Garai, G., Chaudhuri, B.B., Pal, U.: Online Handwritten Indian Script Recognition: A Human Motor Function Based Framework. In: 16th Int’l Conference on Pattern Recognition, vol. 3, pp. 164–167 (2002)Google Scholar
  8. 8.
    Gers, F., Schmidhuber, J.: Recurrent Nets that Time and Count. In: IEEE-INNS-ENNS Joint Conf. on Neural Networks, vol. 3, pp. 189–194 (2000)Google Scholar
  9. 9.
    Graves, A.: Offline Arabic Handwriting Recognition with Multidimensional Neural Networks. In: Guide to OCR for Arabic Scripts, pp. 297–314. Springer (2012)Google Scholar
  10. 10.
    Graves, A., Fernández, S., Gomez, F., Schmidhuber, J.: Connectionist Temporal Classification: Labelling Unsegmented Sequential Data with Recurrent Neural Networks. In: 23rd Int’l Conf. on Machine Learning, pp. 369–376 (2006)Google Scholar
  11. 11.
    Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A Novel Connectionist System for Unconstrained Handwriting Recognition. IEEE Transaction on Pattern Analysis and Machine Intelligence 31(5), 855–868 (2009)CrossRefGoogle Scholar
  12. 12.
    Graves, A., Schmidhuber, J.: Framewise Phoneme Classification with Bidirectional LSTM Networks. In: Int’l Joint Conf. on Neural Networks, vol. 4, pp. 2047–2052 (2005)Google Scholar
  13. 13.
    Katayama, Y., Uchida, S., Sakoe, H.: A new HMM for On-Line Character Recognition using Pen-Direction and Pen-Coordinate Features. In: 19th Int’l Conf. on Pattern Recognition, pp. 1–4 (2008)Google Scholar
  14. 14.
    Kittler, J., Hatef, M., Duin, R.P., Matas, J.: On Combining Classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)CrossRefGoogle Scholar
  15. 15.
    Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience (2004)Google Scholar
  16. 16.
    Mondal, T., Bhattacharya, U., Parui, S.K., Das, K., Mandalapu, D.: On-line Handwriting Recognition of Indian Scripts – The First Benchmark. In: Int’l Conf. of Frontiers in Handwriting Recognition, pp. 200–205 (2010)Google Scholar
  17. 17.
    Nakagawa, M., Tokuno, J., Zhu, B., Onuma, M., Oda, H., Kitadai, A.: Recent Results of Online Japanese Handwriting Recognition and its Applications. In: Doermann, D., Jaeger, S. (eds.) SACH 2006. LNCS, vol. 4768, pp. 170–195. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  18. 18.
    Parui, S.K., Bhattacharya, U., Chaudhuri, B.B.: Online Handwritten Bangla Character Recognition Using HMM. In: Int’l Conf. on Pattern Recognition, pp. 1–4 (2008)Google Scholar
  19. 19.
    Parui, S.K., Bhattacharya, U., Shaw, B., Guin, K.: A Hidden Markov Models for Recognition of Online Handwritten Bangla Numerals. In: 41st National Annual Convention of CSI, pp. 27–31 (2006)Google Scholar
  20. 20.
    Plamondon, R., Srihari, S.N.: On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey. IEEE Transaction on Pattern Analysis and Machine Intelligence 22(1), 63–84 (2000)CrossRefGoogle Scholar
  21. 21.
    Rabiner, L.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  22. 22.
    Roy, K., Sharma, N., Pal, T., Pal, U.: Online bangla handwriting recognition system. In: 6th Int’l Conf. on Advances in Pattern Recognition, pp. 117–122 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Volkmar Frinken
    • 1
  • Nilanjana Bhattacharya
    • 2
  • Seiichi Uchida
    • 1
  • Umapada Pal
    • 3
  1. 1.Department of Advanced Information TechnologyKyushu UniversityFukuokaJapan
  2. 2.Bose InstituteKolkataIndia
  3. 3.Computer Vision and Pattern Recognition UnitIndian Statistical InstituteKolkataIndia

Personalised recommendations