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H-PSO-LSTM: Hybrid LSTM Trained by PSO for Online Handwriter Identification

  • Hounaïda Moalla
  • Walid Elloumi
  • Adel M. Alimi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10637)

Abstract

The automatic writer’s recognition from his manuscript is a topical issue handling online writing. Recurrent neural networks (RNNs) are an effective means of solving such problem. More specifically, RNN networks with Long and Short Term Memory (LSTM) represent an ideal mean for writer’s recognition. Intuitively, LSTM networks are based on the gradient method for their learning processes. In addition, an LSTM node presents a complex data processing machine.

Our hybrid approach combining LSTM and PSO (H-PSO-LSTM) presents the purpose of this paper and increases the performance of the network.

Experiments were carried out on a Biometrics Ideal Test (BIT) bilingual database (Chinese and English). The BIT deals with a large number of writers (between 130 and 188). With H-PSO-LSTM, we were able to improve the learning performance accuracy to 91.9% instead of 81.2%.

Keywords

Recurrent Neural Network Long-Short Term Memory Particle Swarm Optimization Biometrics Ideal Test Random Hybrid Strokes 

Notes

Acknowledgment

The research leading to these results has received funding from the Ministry of Higher Education and Scientific Research of Tunisia under the grant agreement number LR11ES48.

References

  1. 1.
    Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5), 602–610 (2005)CrossRefGoogle Scholar
  2. 2.
    Cai, X., Zhang, N., Venayagamoorthy, G.K., Wunsch, D.C.: Time series prediction with recurrent neural networks using a hybrid PSO-EA algorithm. Neurocomputing 70(13), 2342–2353 (2007)CrossRefGoogle Scholar
  3. 3.
    Dhahri, H., Alimi, M.A.: The modified differential evolution and the RBF (MDE-RBF) neural network for time series prediction. In: IJCNN 2006, International Joint Conference on Neural Network, pp. 2938–2943. IEEE, Vancouver (2006)Google Scholar
  4. 4.
    Boubaker, H., Kherallah, M., Alimi, M.A.: New algorithm of straight or curved baseline detection for short Arabic handwritten writing. In: ICDAR 2009, 10th International Conference on Document Analysis and Recognition, pp. 778–782. IEEE, Barcelona (2009)Google Scholar
  5. 5.
    Slimane, F., Kanoun, S., Hennebert, J., Alimi, M.A., Ingold, R.: A study on font-family and font-size recognition applied to Arabic word images at ultra-low resolution. Pattern Recogn. Lett. 34(2), 209–218 (2013)CrossRefGoogle Scholar
  6. 6.
    Moussa, S.B., Zahour, A., Benabdelhafid, A., Alimi, M.A.: New features using fractal multi-dimensions for generalized Arabic font recognition. Pattern Recogn. Lett. 31(5), 361–371 (2010)CrossRefGoogle Scholar
  7. 7.
    Bezine, H., Alimi, M.A., Derbel, N.: Handwriting trajectory movements controlled by a beta-elliptic model. In: ICDAR 2003, Proceedings of the International Conference on Document Analysis and Recognition, Scotland, pp. 1228–1232 (2003)Google Scholar
  8. 8.
    Alimi, M.A.: Evolutionary computation for the recognition of on-line cursive handwriting. IETE J. Res. 48(5), 385–396 (2002)CrossRefGoogle Scholar
  9. 9.
    Baccour, L., Alimi, M.A., John, R.I.: Similarity measures for intuitionistic fuzzy sets: state of the art. J. Intell. Fuzzy Syst. 24(1), 37–49 (2013)zbMATHMathSciNetGoogle Scholar
  10. 10.
    Chen, K., Yan, Z., Huo, Q.: A context-sensitive-chunk BPTT approach to training deep LSTM/BLSTM recurrent neural networks for offline handwriting recognition. In: ICDAR 2015, 13th International Conference on Document Analysis and Recognition, France, pp. 411–415. IEEE (2015)Google Scholar
  11. 11.
    Gers, F.A., Schmidhuber, E.: LSTM recurrent networks learn simple context-free and context-sensitive languages. IEEE Trans. Neural Netw. 12(6), 1333–1340 (2001)CrossRefGoogle Scholar
  12. 12.
    Zhang, X.Y., Xie, G.S., Liu, C.L., Bengio, Y.: End-to-end online writer identification with recurrent neural network. IEEE Trans. Hum.-Mach. Syst. 74(2), 285–292 (2017)CrossRefGoogle Scholar
  13. 13.
    Elbaati, A., Boubaker, H., Kherallah, M., Ennaji, A., El Abed, H., Alimi, M.A.: Arabic handwriting recognition using restored stroke chronology. In: ICDAR 2009, 10th International Conference on Document Analysis and Recognition, Beijing, China, pp. 411–415. IEEE (2009)Google Scholar
  14. 14.
    Huang, T.Y., Li, C.J., Hsu, T.W.: Structure and parameter learning algorithm of Jordan type recurrent neural networks. In: IJCNN 2007, International Joint Conference Neural Networks, pp. 1819–1824. IEEE, Barcelona (2007)Google Scholar
  15. 15.
    Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. In: ICML 2013, International Conference on Machine Learning, Atlanta, pp. 1310–1318 (2013)Google Scholar
  16. 16.
    Bouaziz, S., Dhahri, H., Alimi, M.A., Abraham, A.: A hybrid learning algorithm for evolving flexible beta basis function neural tree model. Neurocomputing 117, 107–117 (2013)CrossRefGoogle Scholar
  17. 17.
    Zhang, J.R., Zhang, J., Lok, T.M., Lyu, M.R.: A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training. Appl. Math. Comput. 185(2), 1026–1037 (2007)zbMATHGoogle Scholar
  18. 18.
    Elloumi, W., Baklouti, N., Abraham, A., Alimi, M.A.: The multi-objective hybridization of Particle Swarm Optimization and fuzzy ant colony optimization. J. Intell. Fuzzy Syst. 27(1), 515–525 (2014)zbMATHMathSciNetGoogle Scholar
  19. 19.
    Elloumi, W., Alimi, M.A.: A more efficient MOPSO for optimization. In: AICCSA 2010, ACS/IEEE International Conference on Computer System and Applications, Tunisia, pp. 1–7 (2010)Google Scholar
  20. 20.
    Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. (2016)Google Scholar
  21. 21.
    Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks. Springer, Heidelberg (2012)CrossRefzbMATHGoogle Scholar
  22. 22.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  23. 23.
    Poli, R., Kennedy, J., Blackwell, T.: Particle Swarm Optimization. Swarm Intell. 1(1), 33–57 (2007)CrossRefGoogle Scholar
  24. 24.
    Bali, O., Elloumi, W., Abraham, A., Alimi, M.A.: GPU PSO and ACO applied to TSP for vehicle security tracking. J. Inf. Assur. Secur. 11(6), 369–384 (2016)Google Scholar
  25. 25.
    Elloumi, W., Alimi, M.A.: Combinatory optimization of ACO and PSO. In: META 2008, Second International Conference on Metaheuristics and Nature Inspired Computing, Tunisia, pp. 1–8 (2008)Google Scholar
  26. 26.
    Feng, M., Pan, H.: A modified PSO algorithm based on cache replacement algorithm. In: CIS 2014, Computational Intelligence and Security, China, pp. 558–562 (2014)Google Scholar
  27. 27.
    Elloumi, W., El Abed, H., Abraham, A., Alimi, M.A.: A comparative study of the improvement of performance using a PSO modified by ACO applied to TSP. Appl. Soft Comput. 25, 234–241 (2014)CrossRefGoogle Scholar
  28. 28.
    Chouikhi, N., Ammar, B., Rokbani, N., Alimi, M.A.: PSO-based analysis of Echo State Network parameters for time series forecasting. Appl. Soft Comput. 55, 211–225 (2017)CrossRefGoogle Scholar
  29. 29.
    Sanjeevi, S.G., Nikhila, A.N., Khan, T., Sumathi, G.: Hybrid PSO-SA algorithm for training a neural network for classification. Int. J. Comput. Sci. Eng. Appl. (IJCSEA 2011) 1(6), 73–83 (2011)Google Scholar
  30. 30.
    Dhahri, H., Alimi, M.A.: The modified differential evolution and the RBF (MDE-RBF) neural network for time series prediction. In: IJCNN 2006, International Joint Conference on Neural Networks, pp. 2938–2943. IEEE, Vancouver (2006)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hounaïda Moalla
    • 1
  • Walid Elloumi
    • 1
  • Adel M. Alimi
    • 1
  1. 1.REGIM-Lab: Research Groups on Intelligent Machines, National Engineering School of Sfax (ENIS)University of SfaxSfaxTunisia

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