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Multilingual handwritten numeral recognition using a robust deep network joint with transfer learning

Fateh, Amirreza and Fateh, Mansoor and Abolghasemi, Vahid (2021) 'Multilingual handwritten numeral recognition using a robust deep network joint with transfer learning.' Information Sciences, 581. pp. 479-494. ISSN 0020-0255

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Abstract

Numeral recognition plays a crucial role in creating automated systems such as posting address sorting and license plate recognition. Nowadays, numeral recognition systems which have the capability of recognising multiple languages are highly beneficial due to growing international correspondence and transactions, especially in multilingual countries where several languages are used simultaneously. Therefore, handwritten numeral recognition is more challenging than printed numeral recognition due to having different and complex handwriting styles. Hence, developing a multilingual handwritten system is considered as an important and debatable issue. We address this issue by proposing a language-independent model based on a robust CNN. Our proposed model is composed of language recognition and digit recognition, which aims to handle the recognition of multi-script images. We used transfer-learning in the proposed system to enhance the image quality and consequently the recognition performance. Extensive experiments were conducted to verify the effectiveness of both language and digit recognition procedures. The proposed system was tested with six different languages. The results showed an average accuracy of up to 99.8% for recognising various languages and the associated digits. The robustness and design procedure of the proposed model created a cost-effective extension for recognition of handwritten numerals in other languages.

Item Type: Article
Uncontrolled Keywords: Deep learning; Transfer learning; Multilingual; Handwritten numeral recognition
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 15 Dec 2021 14:36
Last Modified: 15 Jan 2022 01:38
URI: http://repository.essex.ac.uk/id/eprint/31898

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