scholarly journals Real Time SIBI Sign Language Recognition Based on K-Nearest Neighbor

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Fitrah Humaira ◽  
Supria Supria ◽  
Darlis Herumurti ◽  
Kukuh Widarsono
Keyword(s):  
Real Time ◽  
Sign Language ◽  
Nearest Neighbor ◽  
Language Recognition ◽  
K Nearest Neighbor ◽  
Sign Language Recognition

scholarly journals Arabic Sign Language Recognition System for Alphabets Using Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Gamal Tharwat ◽  
Abdelmoty M. Ahmed ◽  
Belgacem Bouallegue
Keyword(s):  
Machine Learning ◽  
Sign Language ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Machine Learning Techniques ◽  
Language Recognition ◽  
K Nearest Neighbor ◽  
Sign Language Recognition ◽  
Learning Techniques ◽  
Arabic Sign Language

In recent years, the role of pattern recognition in systems based on human computer interaction (HCI) has spread in terms of computer vision applications and machine learning, and one of the most important of these applications is to recognize the hand gestures used in dealing with deaf people, in particular to recognize the dashed letters in surahs of the Quran. In this paper, we suggest an Arabic Alphabet Sign Language Recognition System (AArSLRS) using the vision-based approach. The proposed system consists of four stages: the stage of data processing, preprocessing of data, feature extraction, and classification. The system deals with three types of datasets: data dealing with bare hands and a dark background, data dealing with bare hands, but with a light background, and data dealing with hands wearing dark colored gloves. AArSLRS begins with obtaining an image of the alphabet gestures, then revealing the hand from the image and isolating it from the background using one of the proposed methods, after which the hand features are extracted according to the selection method used to extract them. Regarding the classification process in this system, we have used supervised learning techniques for the classification of 28-letter Arabic alphabet using 9240 images. We focused on the classification for 14 alphabetic letters that represent the first Quran surahs in the Quranic sign language (QSL). AArSLRS achieved an accuracy of 99.5% for the K-Nearest Neighbor (KNN) classifier.

Hear Sign Language: A Real-time End-to-End Sign Language Recognition System

2020 ◽  
pp. 1-1
Author(s):  
Zhibo Wang ◽  
Tengda Zhao ◽  
Jinxin Ma ◽  
Hongkai Chen ◽  
Kaixin Liu ◽  
...  
Keyword(s):  
Real Time ◽  
Sign Language ◽  
Recognition System ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
End To End
Sign in / Sign up

Export Citation Format

Share Document