Arabic Sign Language Recognition Using Radial Signature and Dynamic Time Warping

2013 ◽  
Vol 7 ◽  
pp. 97-115
Author(s):  
Abdelatif Hussein A. Ali ◽  
Ramzy Karam
Keyword(s):  
Sign Language ◽  
Dynamic Time Warping ◽  
Language Recognition ◽  
Time Warping ◽  
Sign Language Recognition ◽  
Arabic Sign Language ◽  
Dynamic Time

scholarly journals Sign Language Recognition Using Wearable Electronics: Implementing k-Nearest Neighbors with Dynamic Time Warping and Convolutional Neural Network Algorithms

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3879 ◽  
Author(s):  
Giovanni Saggio ◽  
Pietro Cavallo ◽  
Mariachiara Ricci ◽  
Vito Errico ◽  
Jonathan Zea ◽  
...  
Keyword(s):  
Sign Language ◽  
Dynamic Time Warping ◽  
Parametric Method ◽  
Nearest Neighbors ◽  
Wearable Electronics ◽  
Language Recognition ◽  
Time Warping ◽  
Sign Language Recognition ◽  
K Nearest Neighbors ◽  
Dynamic Time

We propose a sign language recognition system based on wearable electronics and two different classification algorithms. The wearable electronics were made of a sensory glove and inertial measurement units to gather fingers, wrist, and arm/forearm movements. The classifiers were k-Nearest Neighbors with Dynamic Time Warping (that is a non-parametric method) and Convolutional Neural Networks (that is a parametric method). Ten sign-words were considered from the Italian Sign Language: cose, grazie, maestra, together with words with international meaning such as google, internet, jogging, pizza, television, twitter, and ciao. The signs were repeated one-hundred times each by seven people, five male and two females, aged 29–54 y ± 10.34 (SD). The adopted classifiers performed with an accuracy of 96.6% ± 3.4 (SD) for the k-Nearest Neighbors plus the Dynamic Time Warping and of 98.0% ± 2.0 (SD) for the Convolutional Neural Networks. Our system was made of wearable electronics among the most complete ones, and the classifiers top performed in comparison with other relevant works reported in the literature.

scholarly journals Chinese Sign Language Recognition Based on DTW-Distance-Mapping Features

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Juan Cheng ◽  
Fulin Wei ◽  
Yu Liu ◽  
Chang Li ◽  
Qiang Chen ◽  
...  
Keyword(s):  
Sign Language ◽  
Feature Representation ◽  
Communication Tool ◽  
Language Recognition ◽  
Time Warping ◽  
Sign Language Recognition ◽  
Key Points ◽  
Representation Method ◽  
Distance Mapping ◽  
Dynamic Time

Sign language is an important communication tool between the deaf and the external world. As the number of the Chinese deaf accounts for 15% of the world, it is highly urgent to develop a Chinese sign language recognition (CSLR) system. Recently, a novel phonology- and radical-coded CSL, taking advantages of a limited and constant number of coded gestures, has been preliminarily verified to be feasible for practical CSLR systems. The keynote of this version of CSL is that the same coded gesture performed in different orientations has different meanings. In this paper, we mainly propose a novel two-stage feature representation method to effectively characterize the CSL gestures. First, an orientation-sensitive feature is extracted regarding the distances between the palm center and the key points of the hand contour. Second, the extracted features are transformed by a dynamic time warping- (DTW-) based feature mapping approach for better representation. Experimental results demonstrate the effectiveness of the proposed feature extraction and mapping approaches. The averaged classification accuracy of all the 39 types of CSL gestures acquired from 11 subjects exceeds 93% for all the adopted classifiers, achieving significant improvement compared to the scheme without DTW-distance-mapping.

scholarly journals Arabic Sign Language Recognition System Using 2D Hands and Body Skeleton Data

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 59612-59627
Author(s):  
Mohamed A. Bencherif ◽  
Mohammed Algabri ◽  
Mohamed A. Mekhtiche ◽  
Mohammed Faisal ◽  
Mansour Alsulaiman ◽  
...  
Keyword(s):  
Sign Language ◽  
Recognition System ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Arabic Sign Language

KArSL

2021 ◽  
Vol 20 (1) ◽  
pp. 1-19
Author(s):  
Ala Addin I. Sidig ◽  
Hamzah Luqman ◽  
Sabri Mahmoud ◽  
Mohamed Mohandes
Keyword(s):  
Deep Learning ◽  
Sign Language ◽  
Markov Models ◽  
Body Language ◽  
Arab Countries ◽  
Microsoft Kinect ◽  
Classification Model ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Arabic Sign Language

Sign language is the major means of communication for the deaf community. It uses body language and gestures such as hand shapes, lib patterns, and facial expressions to convey a message. Sign language is geography-specific, as it differs from one country to another. Arabic Sign language is used in all Arab countries. The availability of a comprehensive benchmarking database for ArSL is one of the challenges of the automatic recognition of Arabic Sign language. This article introduces KArSL database for ArSL, consisting of 502 signs that cover 11 chapters of ArSL dictionary. Signs in KArSL database are performed by three professional signers, and each sign is repeated 50 times by each signer. The database is recorded using state-of-art multi-modal Microsoft Kinect V2. We also propose three approaches for sign language recognition using this database. The proposed systems are Hidden Markov Models, deep learning images’ classification model applied on an image composed of shots of the video of the sign, and attention-based deep learning captioning system. Recognition accuracies of these systems indicate their suitability for such a large number of Arabic signs. The techniques are also tested on a publicly available database. KArSL database will be made freely available for interested researchers.

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