Multi-lingual character handwriting framework based on an integrated deep learning based sequence-to-sequence attention model

Rabhi, Besma; Elbaati, Abdelkarim; Boubaker, Houcine; Hamdi, Yahia; Hussain, Amir; Alimi, Adel M.

Abstract

Online signals are rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down movements. Their offline counterparts consist of a set of pixels. Thus, online handwriting recognition accuracy is generally better than offline. In this paper, we propose an original framework for recovering temporal order and pen velocity from offline multi-lingual handwriting. Our framework is based on an integrated sequence-to-sequence attention model. The proposed system involves extracting a hidden representation from an image using a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BGRU), and decoding the encoded vectors to generate dynamic information using a BGRU with temporal attention. We validate our framework using an online recognition system applied to a benchmark Latin, Arabic and Indian On/Off dual-handwriting character database. The performance of the proposed multi-lingual system is demonstrated through a low error rate of point coordinates and high accuracy system rate.

Citation

Rabhi, B., Elbaati, A., Boubaker, H., Hamdi, Y., Hussain, A., & Alimi, A. M. (2021). Multi-lingual character handwriting framework based on an integrated deep learning based sequence-to-sequence attention model. Memetic Computing, 13, Article 459-475. https://doi.org/10.1007/s12293-021-00345-6

• Journal Article Type: Article
• Acceptance Date: Sep 3, 2021
• Online Publication Date: Sep 18, 2021
• Publication Date: 2021-12
• Deposit Date: Oct 18, 2021
• Journal: Memetic Computing
• Print ISSN: 1865-9284
• Electronic ISSN: 1865-9292
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Article Number: 459-475
• DOI: https://doi.org/10.1007/s12293-021-00345-6
• Keywords: Temporal order recovery, Pen velocity reconstruction, Deep learning, BGRU, Attention model
• Public URL: http://researchrepository.napier.ac.uk/Output/2811606