scholarly journals Sign Language Recognition-A Survey of Techniques

2020 ◽  
Vol 9 (2) ◽  
pp. 854-857
Keyword(s):  
Sign Language ◽  
Recognition System ◽  
Spoken Language ◽  
Hearing Impaired ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Sign Languages ◽  
Data Glove ◽  
Depth Sensors ◽  
Impaired People

Sign language is the only method of communication for the hearing and speech impaired people around the world. Most of the speech and hearing-impaired people understand single sign language. Thus, there is an increasing demand for sign language interpreters. For regular people learning sign language is difficult, and for speech and hearing-impaired person, learning spoken language is impossible. There is a lot of research being done in the domain of automatic sign language recognition. Different methods such as, computer vision, data glove, depth sensors can be used to train a computer to interpret sign language. The interpretation is being done from sign to text, text to sign, speech to sign and sign to speech. Different countries use different sign languages, the signers of different sign languages are unable to communicate with each other. Analyzing the characteristic features of gestures provides insights about the sign language, some common features in sign languages gestures will help in designing a sign language recognition system. This type of system will help in reducing the communication gap between sign language users and spoken language users.

scholarly journals Sensor Fusion of Motion-Based Sign Language Interpretation with Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6256
Author(s):  
Boon Giin Lee ◽  
Teak-Wei Chong ◽  
Wan-Young Chung
Keyword(s):  
Deep Learning ◽  
American Sign Language ◽  
Sign Language ◽  
Sensor Fusion ◽  
Recognition Rate ◽  
Recognition System ◽  
Hearing Impaired ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Impaired People

Sign language was designed to allow hearing-impaired people to interact with others. Nonetheless, knowledge of sign language is uncommon in society, which leads to a communication barrier with the hearing-impaired community. Many studies of sign language recognition utilizing computer vision (CV) have been conducted worldwide to reduce such barriers. However, this approach is restricted by the visual angle and highly affected by environmental factors. In addition, CV usually involves the use of machine learning, which requires collaboration of a team of experts and utilization of high-cost hardware utilities; this increases the application cost in real-world situations. Thus, this study aims to design and implement a smart wearable American Sign Language (ASL) interpretation system using deep learning, which applies sensor fusion that “fuses” six inertial measurement units (IMUs). The IMUs are attached to all fingertips and the back of the hand to recognize sign language gestures; thus, the proposed method is not restricted by the field of view. The study reveals that this model achieves an average recognition rate of 99.81% for dynamic ASL gestures. Moreover, the proposed ASL recognition system can be further integrated with ICT and IoT technology to provide a feasible solution to assist hearing-impaired people in communicating with others and improve their quality of life.

scholarly journals Sign Language Recognition for Sentence Level Continuous Signings

2021 ◽  
Author(s):  
Ishika Godage ◽  
Ruvan Weerasignhe ◽  
Damitha Sandaruwan
Keyword(s):  
Sign Language ◽  
Human Life ◽  
Vital Role ◽  
Hearing Impaired ◽  
Leap Motion ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
The People ◽  
Sentence Level ◽  
Impaired People

It is no doubt that communication plays a vital role in human life. There is, however, a significant population of hearing-impaired people who use non-verbal techniques for communication, which a majority of the people cannot understand. The predominant of these techniques is based on sign language, the main communication protocol among hearing impaired people. In this research, we propose a method to bridge the communication gap between hearing impaired people and others, which translates signed gestures into text. Most existing solutions, based on technologies such as Kinect, Leap Motion, Computer vision, EMG and IMU try to recognize and translate individual signs of hearing impaired people. The few approaches to sentence-level sign language recognition suffer from not being user-friendly or even practical owing to the devices they use. The proposed system is designed to provide full freedom to the user to sign an uninterrupted full sentence at a time. For this purpose, we employ two Myo armbands for gesture-capturing. Using signal processing and supervised learning based on a vocabulary of 49 words and 346 sentences for training with a single signer, we were able to achieve 75-80% word-level accuracy and 45-50% sentence level accuracy using gestural (EMG) and spatial (IMU) features for our signer-dependent experiment.

scholarly journals Vision-based Pakistani Sign Language Recognition Using Bag-of-Words and Support Vector Machines

2022 ◽  
Author(s):  
Muhammad Shaheer Mirza ◽  
Sheikh Muhammad Munaf ◽  
Shahid Ali ◽  
Fahad Azim ◽  
Saad Jawaid Khan
Keyword(s):  
Sign Language ◽  
Recognition System ◽  
Support Vector ◽  
Bag Of Words ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Sign Languages ◽  
Vector Machines ◽  
Rgb Images ◽  
Native Signers

Abstract In order to perform their daily activities, a person is required to communicating with others. This can be a major obstacle for the deaf population of the world, who communicate using sign languages (SL). Pakistani Sign Language (PSL) is used by more than 250,000 deaf Pakistanis. Developing a SL recognition system would greatly facilitate these people. This study aimed to collect data of static and dynamic PSL alphabets and to develop a vision-based system for their recognition using Bag-of-Words (BoW) and Support Vector Machine (SVM) techniques. A total of 5,120 images for 36 static PSL alphabet signs and 353 videos with 45,224 frames for 3 dynamic PSL alphabet signs were collected from 10 native signers of PSL. The developed system used the collected data as input, resized the data to various scales and converted the RGB images into grayscale. The resized grayscale images were segmented using Thresholding technique and features were extracted using Speeded Up Robust Feature (SURF). The obtained SURF descriptors were clustered using K-means clustering. A BoW was obtained by computing the Euclidean distance between the SURF descriptors and the clustered data. The codebooks were divided into training and testing using 5-fold cross validation. The highest overall classification accuracy for static PSL signs was 97.80% at 750×750 image dimensions and 500 Bags. For dynamic PSL signs a 96.53% accuracy was obtained at 480×270 video resolution and 200 Bags.

Developing Amharic Sign Language Recognition Model for Amharic Characters Using Deep Learning Approach

2021 ◽  
Author(s):  
Isayas Feyera ◽  
Hussien Seid
Keyword(s):  
Neural Network ◽  
Sign Language ◽  
Hearing Impaired ◽  
Single Shot ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Recognition Model ◽  
Training Models ◽  
The People ◽  
Impaired People

Abstract Hearing-impaired people use Sign Language to communicate with each other as well as with other communities. Usually, they are unable to communicate with normal people. Most of the people without hearing disability do not understand the Sign Language and unable to understand hearing-impaired people. So, they need recognition of Sign Language to text. In this research, a model is optimized for the recognition of Amharic Sign Language to Amharic characters. A convolutional neural network model is trained on datasets gathered from a teacher of Amharic Sign Language. Frame extraction from Amharic Sign Language video, labeling and annotation, XML creation, generate TFrecord, and training models are major general steps followed for developing models to recognize Amharic Sign Language to characters. After training of the neural network is completed, the model is saved for recognition of Sign Language from a video system or from the frame of video. The accuracy of the model is the summation of confidence of individual alphabets correctly recognized divided by the number of alphabets presented for evaluation for Faster R-CNN and SSD. Hence, the mean average accuracy of the Faster R-CNN and Single-Shot Detector is found to be 98. 25% and 96 % respectively. The model is trained and evaluated for the character of the Amharic language. The research will continue to include the remaining words and sentence used in Amharic Sign Language to have a full- edged Sign Language recognition model to a complete system.

scholarly journals AUTOMATIC LIP-READING OF HEARING IMPAIRED PEOPLE

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 97-101
Author(s):  
D. Ivanko ◽  
D. Ryumin ◽  
A. Karpov
Keyword(s):  
Sign Language ◽  
Assistive Technologies ◽  
Hearing Impaired ◽  
Visual Speech ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Hand Gestures ◽  
Human Machine Interaction ◽  
Lip Reading ◽  
Impaired People

<p><strong>Abstract.</strong> Inability to use speech interfaces greatly limits the deaf and hearing impaired people in the possibility of human-machine interaction. To solve this problem and to increase the accuracy and reliability of the automatic Russian sign language recognition system it is proposed to use lip-reading in addition to hand gestures recognition. Deaf and hearing impaired people use sign language as the main way of communication in everyday life. Sign language is a structured form of hand gestures and lips movements involving visual motions and signs, which is used as a communication system. Since sign language includes not only hand gestures, but also lip movements that mimic vocalized pronunciation, it is of interest to investigate how accurately such a visual speech can be recognized by a lip-reading system, especially considering the fact that the visual speech of hearing impaired people is often characterized with hyper-articulation, which should potentially facilitate its recognition. For this purpose, thesaurus of Russian sign language (TheRusLan) collected in SPIIRAS in 2018–19 was used. The database consists of color optical FullHD video recordings of 13 native Russian sign language signers (11 females and 2 males) from “Pavlovsk boarding school for the hearing impaired”. Each of the signers demonstrated 164 phrases for 5 times. This work covers the initial stages of this research, including data collection, data labeling, region-of-interest detection and methods for informative features extraction. The results of this study can later be used to create assistive technologies for deaf or hearing impaired people.</p>

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