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 Paper › peer-review

43 Citations (Scopus)

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
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
Publication status	Published - 2 Oct 2018
Event	2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018 - London, United Kingdom Duration: 12 Mar 2018 → 14 Mar 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	12/03/18 → 14/03/18

Access to Document

10.1109/ASAR.2018.8480289

Cite this

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

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).

@inproceedings{2c1b369fef9c4aeaadf85934adab403f,

title = "Deep Convolutional Neural Network for Recognition of Unified Multi-Language Handwritten Numerals",

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.",

keywords = "Arabic Numberals, Deep Convolutional Neural Networks, Hand Written Numerals, Mul-Language Numerals Recognition",

author = "Ghazanfar Latif and Jaafar Alghazo and Loay Alzubaidi and Naseer, \{M. Muzzamal\} and Yazan Alghazo",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018 ; Conference date: 12-03-2018 Through 14-03-2018",

year = "2018",

month = oct,

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doi = "10.1109/ASAR.2018.8480289",

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series = "2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018",

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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, 12/03/18. 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 Paper › peer-review

TY - GEN

T1 - Deep Convolutional Neural Network for Recognition of Unified Multi-Language Handwritten Numerals

AU - Latif, Ghazanfar

AU - Alghazo, Jaafar

AU - Alzubaidi, Loay

AU - Naseer, M. Muzzamal

AU - Alghazo, Yazan

PY - 2018/10/2

Y1 - 2018/10/2

N2 - 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.

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.

KW - Arabic Numberals

KW - Deep Convolutional Neural Networks

KW - Hand Written Numerals

KW - Mul-Language Numerals Recognition

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

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Y2 - 12 March 2018 through 14 March 2018

ER -

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

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

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