Deep Convolutional Neural Network for Recognition of Unified Multi-Language Handwritten Numerals

Ghazanfar Latif; Jaafar Alghazo; Loay Alzubaidi; M. Muzzamal Naseer; Yazan Alghazo

doi:10.1109/ASAR.2018.8480289

Deep Convolutional Neural Network for Recognition of Unified Multi-Language Handwritten Numerals

Ghazanfar Latif, Jaafar Alghazo, Loay Alzubaidi, M. Muzzamal Naseer, Yazan Alghazo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

39 Scopus citations

Abstract

Deep learning systems have recently gained importance as the architecture of choice in artificial intelligence (AI). Handwritten numeral recognition is essential for the development of systems that can accurately recognize digits in different languages which is a challenging task due to variant writing styles. This is still an open area of research for developing an optimized Multilanguage writer independent technique for numerals. In this paper, we propose a deep learning architecture for the recognition of handwritten Multilanguage (mixed numerals belongs to multiple languages) numerals (Eastern Arabic, Persian, Devanagari, Urdu, Western Arabic). The overall accuracy of the combined Multilanguage database was 99.26% with a precision of 99.29% on average. The average accuracy of each individual language was found to be 99.322%. Results indicate that the proposed deep learning architecture produces better results compared to methods suggested in the previous literature.

Original language	English (US)
Title of host publication	2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	90-95
Number of pages	6
ISBN (Electronic)	9781538614594
DOIs	https://doi.org/10.1109/ASAR.2018.8480289
State	Published - Oct 2 2018
Externally published	Yes
Event	2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018 - London, United Kingdom Duration: Mar 12 2018 → Mar 14 2018

Publication series

Name	2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018

Conference

Conference	2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018
Country/Territory	United Kingdom
City	London
Period	3/12/18 → 3/14/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Arabic Numberals
Deep Convolutional Neural Networks
Hand Written Numerals
Mul-Language Numerals Recognition

Publisher link

10.1109/ASAR.2018.8480289

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Latif, G., Alghazo, J., Alzubaidi, L., Naseer, M. M., & Alghazo, Y. (2018). Deep Convolutional Neural Network for Recognition of Unified Multi-Language Handwritten Numerals. In 2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018 (pp. 90-95). Article 8480289 (2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASAR.2018.8480289

Deep Convolutional Neural Network for Recognition of Unified Multi-Language Handwritten Numerals. / Latif, Ghazanfar; Alghazo, Jaafar; Alzubaidi, Loay et al.
2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 90-95 8480289 (2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Latif, G, Alghazo, J, Alzubaidi, L, Naseer, MM & Alghazo, Y 2018, Deep Convolutional Neural Network for Recognition of Unified Multi-Language Handwritten Numerals. in 2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018., 8480289, 2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018, Institute of Electrical and Electronics Engineers Inc., pp. 90-95, 2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018, London, United Kingdom, 3/12/18. https://doi.org/10.1109/ASAR.2018.8480289

Latif G, Alghazo J, Alzubaidi L, Naseer MM, Alghazo Y. Deep Convolutional Neural Network for Recognition of Unified Multi-Language Handwritten Numerals. In 2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 90-95. 8480289. (2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018). doi: 10.1109/ASAR.2018.8480289

Latif, Ghazanfar ; Alghazo, Jaafar ; Alzubaidi, Loay et al. / Deep Convolutional Neural Network for Recognition of Unified Multi-Language Handwritten Numerals. 2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 90-95 (2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018).

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abstract = "Deep learning systems have recently gained importance as the architecture of choice in artificial intelligence (AI). Handwritten numeral recognition is essential for the development of systems that can accurately recognize digits in different languages which is a challenging task due to variant writing styles. This is still an open area of research for developing an optimized Multilanguage writer independent technique for numerals. In this paper, we propose a deep learning architecture for the recognition of handwritten Multilanguage (mixed numerals belongs to multiple languages) numerals (Eastern Arabic, Persian, Devanagari, Urdu, Western Arabic). The overall accuracy of the combined Multilanguage database was 99.26% with a precision of 99.29% on average. The average accuracy of each individual language was found to be 99.322%. Results indicate that the proposed deep learning architecture produces better results compared to methods suggested in the previous literature.",

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AB - Deep learning systems have recently gained importance as the architecture of choice in artificial intelligence (AI). Handwritten numeral recognition is essential for the development of systems that can accurately recognize digits in different languages which is a challenging task due to variant writing styles. This is still an open area of research for developing an optimized Multilanguage writer independent technique for numerals. In this paper, we propose a deep learning architecture for the recognition of handwritten Multilanguage (mixed numerals belongs to multiple languages) numerals (Eastern Arabic, Persian, Devanagari, Urdu, Western Arabic). The overall accuracy of the combined Multilanguage database was 99.26% with a precision of 99.29% on average. The average accuracy of each individual language was found to be 99.322%. Results indicate that the proposed deep learning architecture produces better results compared to methods suggested in the previous literature.

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Deep Convolutional Neural Network for Recognition of Unified Multi-Language Handwritten Numerals

Abstract

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