@article{scholars14301,
            year = {2021},
       publisher = {MDPI},
         journal = {Sensors},
             doi = {10.3390/s21217306},
          volume = {21},
            note = {cited By 8},
          number = {21},
           title = {A sequential handwriting recognition model based on a dynamically configurable crnn},
        keywords = {Character recognition; Convolution; Convolutional neural networks; Recurrent neural networks, Configuration search; Deep learning; Handwriting recognition; Hill climbing; Metaheuristic optimization; Neural architecture search; Neural architectures; Recognition models; Salp swarms; Swarm optimization algorithms, Structural optimization, algorithm; handwriting, Algorithms; Handwriting; Neural Networks, Computer},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118255699&doi=10.3390\%2fs21217306&partnerID=40&md5=ab4d374b7eef1b3814398111aee09a49},
        abstract = {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. {\^A}{\copyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.},
            issn = {14248220},
          author = {Al-Saffar, A. and Awang, S. and Al-Saiagh, W. and Al-Khaleefa, A. S. and Abed, S. A.}
}