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)


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%.


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



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.


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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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