| Autor: |
Al-Saffar A; Faculty of Computing, Universiti Malaysia Pahang (UMP), Gambang 26600, Pahang, Malaysia., Awang S; Faculty of Computing, Universiti Malaysia Pahang (UMP), Gambang 26600, Pahang, Malaysia.; Centre for Data Science and Artificial Intelligence (Data Science Centre), Universiti Malaysia Pahang, Kuantan 26300, Pahang, Malaysia., Al-Saiagh W; Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia UKM, Bangi 43600, Selangor, Malaysia., Al-Khaleefa AS; Department of Computer Engineering, Faculty of Information Technology, Imam Jafar Al-Sadiq University, Tehran 10011, Iraq., Abed SA; Computer and Information Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia. |
| Abstrakt: |
Handwriting recognition refers to recognizing a handwritten input that includes character(s) or digit(s) based on an image. Because most applications of handwriting recognition in real life contain sequential text in various languages, there is a need to develop a dynamic handwriting recognition system. Inspired by the neuroevolutionary technique, this paper proposes a Dynamically Configurable Convolutional Recurrent Neural Network (DC-CRNN) for the handwriting recognition sequence modeling task. The proposed DC-CRNN is based on the Salp Swarm Optimization Algorithm (SSA), which generates the optimal structure and hyperparameters for Convolutional Recurrent Neural Networks (CRNNs). In addition, we investigate two types of encoding techniques used to translate the output of optimization to a CRNN recognizer. Finally, we proposed a novel hybridized SSA with Late Acceptance Hill-Climbing (LAHC) to improve the exploitation process. We conducted our experiments on two well-known datasets, IAM and IFN/ENIT, which include both the Arabic and English languages. The experimental results have shown that LAHC significantly improves the SSA search process. Therefore, the proposed DC-CRNN outperforms the handcrafted CRNN methods. |
