Skip to main content
SpringerLink
Log in

Multi-language online handwriting recognition based on beta-elliptic model and hybrid TDNN-SVM classifier

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Recently, several researches were carried on handwritten document analysis field thanks to the evolution of data capture technologies. For a given document, multiple components could be treated as text, signatures and graphics. In this study, we present a new framework for a Multilanguage online handwritten text analysis where both script identification and recognition are made. The proposed system proceeds by segmenting the script into continuous trajectories delimited between two successive pen-down and pen-up moments. These segments are clustered and trained using Time Delay Neural Network (TDNN) according to their beta-elliptic parameters. In script identification process, the segments belonging to the same script are gathered and brought to a Recurrent Neural Network with Long Short Term Memory (RNN-LSTM) in order to identify its language. For script recognition stage, the samples from the already selected language database are trained and tested using the fuzzy output description obtained by the TDNN coupled to a Support Vector Machines (SVM). The Experiments were made on a large multi-language database containing 45686 online handwriting words from Latin, Arabic and digit scripts and shows very promising results that exceed the recognition rate of 99%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Ahmed H, Azeem SA (2011) On-line Arabic handwriting recognition system based on HMM. In: International conference on document analysis and recognition (ICDAR). IEEE, pp 1324–1328

  2. Alimi AM (2003) Beta neuro-fuzzy systems. Task Q 7(1):23–41

    Google Scholar 

  3. Alimolu F, Alpaydin E (2001) Combining multiple representations for pen-based handwritten digit recognition. Turkish J Electr Eng Comput Sci 9(1):1–12

    Google Scholar 

  4. Bhattacharya S, Maitra DS, Bhattacharya U, Parui SK (2016) An end-to-end system for Bangla online handwriting recognition. In: 15th international conference on frontiers in handwriting recognition (ICFHR). IEEE, pp 373–378

  5. Boubaker H, Elbaati A, Tagougui N, El Abed H, Kherallah M, Alimi AM (2012) Online Arabic databases and applications. In: Guide to OCR for Arabic scripts. Springer, London, pp 541–557

    Chapter  Google Scholar 

  6. Boubaker H, Rezzoug N, Kherallah M, Gorce P, Alimi AM (2015) Spatiotemporal representation of 3D hand trajectory based on Beta-elliptic models. Comput Methods Biomech Biomed Eng J 18(15):1632–1647

    Article  Google Scholar 

  7. Bezine H, Alimi AM, Sherkat N (2004) Generation and analysis of handwriting script with the beta-elliptic model. In: Proceedings of the 9th international workshop on frontiers in handwriting recognition, pp 515–520

  8. Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121–167

    Article  Google Scholar 

  9. Elleuch M, Zouari R, Kherallah M (2016) Feature extractor based deep method to enhance online arabic handwritten recognition system. In: 25th international conference on artificial neural networks, pp 136–144

  10. Gargouri M, Kanoun S, Ogier JM (2013) Text-independent writer identification on online Arabic handwriting. In: 12th international conference on document analysis and recognition (ICDAR). IEEE, pp 428–432

  11. Graves A, Liwicki M, Fernández S, Bunke H, Bertolami R, Schmidhuber J (2009) A novel connectionist system for unconstrained handwriting recognition. IEEE Trans Pattern Anal Mach Intell 31(5):855–868

    Article  Google Scholar 

  12. Ghosh R, Roy PP (2015) Study of two zone-based features for online Bengali and Devanagari character recognition. In: 13th international conference on document analysis and recognition (ICDAR). IEEE, pp 401–405

  13. Ghosh R, Roy PP (2016) Comparison of zone-features for online Bengali and Devanagari word recognition using HMM. In: 15th international conference on frontiers in handwriting recognition (ICFHR), IEEE, pp 435–440

  14. Guyon I, Schomaker L, Plamondon R, Liberman M, Janet S (1994) UNIPEN project of on-line data exchange and recognizer benchmarks. In: Pattern recognition, vol 2, pp 29–33

  15. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  16. Hollerbach J (1981) An oscillation theory of handwriting. Biol Cybern 39(29, Issue 2):139–156

    Article  Google Scholar 

  17. Indhu TR, Vidya V, Bhadran VK (2015) Multilingual online handwriting recognition system: an android app. In: The fifth international conference on advances in computing and communications (ICACC), IEEE, pp 33–36

  18. Keysers D, Deselaers T, Rowley HA, Wang LL, Carbune V (2017) Multi-language online handwriting recognition. IEEE Trans Pattern Anal Mach Intell 39(6):1180–1194

    Article  Google Scholar 

  19. Kherallah M, Njah S, Alimi AM, Derbel N (2002) Recognition of on-line handwritten digits by neural networks using circular and beta approaches. In: International conference on system: man and cybernetics, IEEE, pp 164–169

  20. Kherallah M, Haddad L, Alimi AM, Mitiche A (2008) On-line handwritten digit recognition based on trajectory and velocity modeling. Pattern Recog Lett 29 (5):580–594

    Article  Google Scholar 

  21. Kherallah M, Bouri F, Alimi AM (2009) On-line Arabic handwriting recognition system based on visual encoding and genetic algorithm. Eng Appl Artif Intell 22 (1):153–170

    Article  Google Scholar 

  22. Kherallah M, Tagougui N, Alimi AM, El Abed H, Margner V (2011) Online Arabic handwriting recognition competition. In: International conference on document analysis and recognition (ICDAR). IEEE, pp 1454–1458

  23. Khlif H, Prum S, Kessentini Y, Kanoun S, Ogier J (2016) Fusion of explicit segmentation based system and segmentation-free based system for on-line Arabic handwritten word recognition. In: 15th international conference on frontiers in handwriting recognition (ICFHR). IEEE, pp 399–4041

  24. Lee J, Kim JH (1996) A unified network-based approach for online recognition of multi-lingual cursive handwritings. In: Proceeding of fifth international workshop on frontiers in handwriting recognition, pp 393–397

  25. Maalej R, Tagougui N, Kherallah M (2016) Online Arabic handwriting recognition with dropout applied in deep recurrent neural networks. In: 12th IAPR workshop on document analysis aystems (DAS). IEEE, pp 417–421

  26. MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol 1, no 14, pp 281–297

  27. Namboodiri AM, Jain AK (2004) Online handwritten script recognition. IEEE Trans Pattern Anal Mach Intell 26(1):124–130

    Article  Google Scholar 

  28. Nakkach H, Haboubi S, Amiri H (2016) Online Arabic character recognition using global and local features. In: 3rd international conference on automation control engineering and computer science, pp 120–124

  29. Nguyen T, Zhu B, Nakagawa M (2014) A semi-incremental recognition method for on-line handwritten english text. In: 14th international conference on frontiers in handwriting recognition (ICFHR). IEEE, pp 234–239

  30. O’Reilly C, Plamondon R (2009) Development of a Sigma–Lognormal representation for on-line signatures. Pattern Recog 42(12):3324–3337

    Article  Google Scholar 

  31. Pham V, Bluche T, Kermorvant C, Louradour J (2014) Dropout improves recurrent neural networks for handwriting recognition. In: 14th international conference on frontiers in handwriting recognition (ICFHR). IEEE, pp 285–290

  32. Plamondon R, Alimi AM, Yergeau P, Franck L (1993) Modeling velocity profiles of rapid movements: a comparative study. Biol Cybern 69(2):119–128

    Article  Google Scholar 

  33. Plamondon R (1995) A kinematic theory of rapid human movements. Part-II. Movement time and control. Biol Cybern 72:309–320

    Article  Google Scholar 

  34. Samanta O, Anandarup R, Bhattacharya U, Ujjwal SK (2015) Script independent online handwriting recognition. In: 13th international conference on document analysis and recognition (ICDAR). IEEE, pp 1251–1255

  35. Sun L, Tonghua S, Wang R (2016) Deep LSTM networks for online chinese handwriting recognition. In: 15th international conference on frontiers in handwriting recognition (ICFHR). IEEE, pp 271–276

  36. Tagougui N, Boubaker H, Kherallah M, Alimi AM (2013) A hybrid MLPNN/HMM recognition system for online Arabic handwritten script. In: World congress on computer and information technology (WCCIT). IEEE, pp 1–6

  37. Tan GX, Viard-Gaudin C, Kot AC (2009) Information retrieval model for online handwritten script identification. In: 10th international conference on document analysis and recognition (ICDAR). IEEE, pp 336–340

  38. Vapnik V (1998) Statistical learning theory, vol 1, 1735,1780

  39. Viviani P, Terzuolo C (1982) Trajectory determines movement dynamics. Neuroscience 7(2):431–437

    Article  Google Scholar 

  40. Waibel A, Hanazawa T, Hinton G, Shikano K, Lang KJ (1989) Phoneme recognition using time-delay neural networks. IEEE Trans Acoust Speech Sig Process 37(3):328–339

    Article  Google Scholar 

  41. Yang W, Jin L, Xie Z, Feng Z (2015) Improved deep convolutional neural network for online handwritten Chinese character recognition using domain-specific knowledge. In: 13th international conference on document analysis and recognition (ICDAR). IEEE, pp 551–555

  42. Zhou X, Zhang Y, Tian F, Wang H, Liu C (2014) Minimum-risk training for semi-Markov conditional random fields with application to handwritten Chinese/Japanese text recognition. Pattern Recog 47(5):1904–1916

    Article  Google Scholar 

  43. Zhu B, Shivram A, Govindaraju V, Nakagawa M (2016) Online handwritten cursive word recognition by combining segmentation-free and segmentation-based methods. In: 15th international conference on frontiers in handwriting recognition (ICFHR). IEEE, pp 417–422

  44. Zouari R, Boubaker H, Kherallah M (2016) A time delay neural network for online arabic handwriting recognition. In: International conference on intelligent systems design and applications. Springer, pp 1005–1014

Download references

Acknowledgements

This work was supported by grants from the General Direction of Scientific Research and Technological Renovation (DGRST), Tunisia, under the ARUB program 01/UR/11/02.

Author information

Authors and Affiliations

  1. National School of Engineers of Sfax, University of Sfax, Sfax, Tunisia

    Ramzi Zouari & Houcine Boubaker

  2. Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia

    Monji Kherallah

Authors
  1. Ramzi Zouari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Houcine Boubaker
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Monji Kherallah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ramzi Zouari.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zouari, R., Boubaker, H. & Kherallah, M. Multi-language online handwriting recognition based on beta-elliptic model and hybrid TDNN-SVM classifier. Multimed Tools Appl 78, 12103–12123 (2019). https://doi.org/10.1007/s11042-018-6764-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6764-0

Keywords

Search

Navigation