Abstract
Recent work has shown that neural networks have great potential in the field of handwriting recognition. The advantage of using this type of architecture, besides being robust, is that the network learns the characteristic vectors automatically thanks to the convolution layers. We can say that it creates intelligent filters. In this article we study deep learning in the field of Arab handwritten character in order to have a better understanding of its functioning. In this paper we present the work we have done on convolutional neural networks. First, we explain the theoretical aspects of neural networks, then we present our experimental protocols and we comment on the results obtained.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tsai, C.: Recognizing handwritten Japanese characters using deep convolutional neural networks. Technical report, Stanford University (2016)
Zhong, Z., Sun, L., Huo, Q.: Improved localization accuracy by LocNet for Faster R-CNN based text detection in natural scene images. Pattern Recognition 96, 106986 (2019)
Vibhute, P.M., Deshpande, M.S.: Performance analysis of deskewing techniques for offline OCR. In: Kumar, A., Mozar, S. (eds.) ICCCE 2019. Lecture Notes in Electrical Engineering, vol. 570. Springer, Singapore (2020)
ElAdel, A, Zaied, M., Amar, C.B.: Trained convolutional neural network based on selected beta filters for Arabic letter recognition, 05 March 2018. https://doi.org/10.1002/widm.1250
El-Sawy, A., Loey, M., El-Bakry, H.: Arabic handwritten characters recognition using convolutional neural network. WSEAS Trans. Comput. Res. 5, 11–19 (2017)
Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193–202 (1980)
Elleuch, M., et al.: Optimization of DBN using regularization methods applied for recognizing Arabic handwritten script. In: International Conference on Computational Science (ICCS 2017), Zurich, 12–14 June 2017, vol. 108, pp. 2292–2297 (2017)
Khaled, S.Y.: Arabic handwritten character recognition based on deep convolutional neural networks. Jordanian J. Comput. Inf. Technol. (JJCIT) 3(3) (2017). New Trends in Information Technology
Torki, M., et al.: Window-based descriptors for Arabic handwritten alphabet recognition: a comparative study on a novel dataset, arXiv preprint arXiv:1411.3519 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Elmiad, A.K. (2020). Arab Handwriting Character Recognition Using Deep Learning. In: Serrhini, M., Silva, C., Aljahdali, S. (eds) Innovation in Information Systems and Technologies to Support Learning Research. EMENA-ISTL 2019. Learning and Analytics in Intelligent Systems, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-030-36778-7_45
Download citation
DOI: https://doi.org/10.1007/978-3-030-36778-7_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36777-0
Online ISBN: 978-3-030-36778-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)