Arabic sign language recognition using optical flow-based features and HMM

Ala addin I. Sidig; Hamzah Luqman; Sabri A. Mahmoud

doi:10.1007/978-3-319-59427-9_32

Arabic sign language recognition using optical flow-based features and HMM

Ala addin I. Sidig
, Hamzah Luqman^*
, Sabri A. Mahmoud

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

18 Scopus citations

Abstract

Sign language is the main communication channel of deaf community. It uses gestures and body language such as facial expressions, lib patterns, and hand shapes to convey meaning. Sign language differs from one country to another. Sign language recognition helps in removing barriers between people who understand only spoken language and those who understand sign language. In this work, we propose an algorithm for segmenting videos of signs into sequences of still images and four techniques for Arabic sign language recognition, namely Modified Fourier Transform (MFT), Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG), and combination of HOG and Histogram of Optical Flow (HOG-HOF). These techniques are evaluated using Hidden Markov Model (HMM). The best performance is obtained with MFT features with 99.11% accuracy. This recognition rate shows the correctness and robustness of the proposed signs’ video segmentation algorithm.

Original language	English
Title of host publication	Lecture Notes on Data Engineering and Communications Technologies
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	297-305
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-319-59427-9_32
State	Published - 2018

Publication series

Name	Lecture Notes on Data Engineering and Communications Technologies
Volume	5
ISSN (Print)	2367-4512
ISSN (Electronic)	2367-4520

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2018.

Keywords

Arabic sign language recognition
HMM
HOG
Video segmentation

ASJC Scopus subject areas

Media Technology
Electrical and Electronic Engineering
Computer Science Applications
Computer Networks and Communications
Information Systems

Access to Document

10.1007/978-3-319-59427-9_32

Cite this

Sidig, A. A. I., Luqman, H., & Mahmoud, S. A. (2018). Arabic sign language recognition using optical flow-based features and HMM. In Lecture Notes on Data Engineering and Communications Technologies (pp. 297-305). (Lecture Notes on Data Engineering and Communications Technologies; Vol. 5). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-319-59427-9_32

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abstract = "Sign language is the main communication channel of deaf community. It uses gestures and body language such as facial expressions, lib patterns, and hand shapes to convey meaning. Sign language differs from one country to another. Sign language recognition helps in removing barriers between people who understand only spoken language and those who understand sign language. In this work, we propose an algorithm for segmenting videos of signs into sequences of still images and four techniques for Arabic sign language recognition, namely Modified Fourier Transform (MFT), Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG), and combination of HOG and Histogram of Optical Flow (HOG-HOF). These techniques are evaluated using Hidden Markov Model (HMM). The best performance is obtained with MFT features with 99.11\% accuracy. This recognition rate shows the correctness and robustness of the proposed signs{\textquoteright} video segmentation algorithm.",

keywords = "Arabic sign language recognition, HMM, HOG, Video segmentation",

author = "Sidig, \{Ala addin I.\} and Hamzah Luqman and Mahmoud, \{Sabri A.\}",

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series = "Lecture Notes on Data Engineering and Communications Technologies",

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Sidig, AAI , Luqman, H & Mahmoud, SA 2018, Arabic sign language recognition using optical flow-based features and HMM. in Lecture Notes on Data Engineering and Communications Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol. 5, Springer Science and Business Media Deutschland GmbH, pp. 297-305. https://doi.org/10.1007/978-3-319-59427-9_32

Arabic sign language recognition using optical flow-based features and HMM. / Sidig, Ala addin I.; Luqman, Hamzah; Mahmoud, Sabri A.
Lecture Notes on Data Engineering and Communications Technologies. Springer Science and Business Media Deutschland GmbH, 2018. p. 297-305 (Lecture Notes on Data Engineering and Communications Technologies; Vol. 5).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Arabic sign language recognition using optical flow-based features and HMM

AU - Sidig, Ala addin I.

AU - Luqman, Hamzah

AU - Mahmoud, Sabri A.

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AB - Sign language is the main communication channel of deaf community. It uses gestures and body language such as facial expressions, lib patterns, and hand shapes to convey meaning. Sign language differs from one country to another. Sign language recognition helps in removing barriers between people who understand only spoken language and those who understand sign language. In this work, we propose an algorithm for segmenting videos of signs into sequences of still images and four techniques for Arabic sign language recognition, namely Modified Fourier Transform (MFT), Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG), and combination of HOG and Histogram of Optical Flow (HOG-HOF). These techniques are evaluated using Hidden Markov Model (HMM). The best performance is obtained with MFT features with 99.11% accuracy. This recognition rate shows the correctness and robustness of the proposed signs’ video segmentation algorithm.

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PB - Springer Science and Business Media Deutschland GmbH

ER -

Arabic sign language recognition using optical flow-based features and HMM

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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