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%.
CITATION STYLE
Moalla, H., Elloumi, W., & Alimi, A. M. (2017). H-PSO-LSTM: Hybrid LSTM Trained by PSO for Online Handwriter Identification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10637 LNCS, pp. 41–50). Springer Verlag. https://doi.org/10.1007/978-3-319-70093-9_5
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