Flex Sensor Based Glove to Control Wheel Chair and Sign Language Translator for Speech Impaired People

2020 ◽  
Author(s):  
Ankit Bansode
Keyword(s):  
Sign Language ◽  
Impaired People ◽  
Wheel Chair ◽  
Control Wheel

Indian Sign Language Recognition on PYNQ Board

2020 ◽  
Vol 13 ◽  
Author(s):  
Sukhendra Singh ◽  
G. N. Rathna ◽  
Vivek Singhal
Keyword(s):  
Sign Language ◽  
Hand Gesture ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Hand Gestures ◽  
Depth Images ◽  
Kinect Camera ◽  
Impaired People ◽  
Web Camera ◽  
Indian Sign Language

Introduction: Sign language is the only way to communicate for speech-impaired people. But this sign language is not known to normal people so this is the cause of barrier in communicating. This is the problem faced by speech impaired people. In this paper, we have presented our solution which captured hand gestures with Kinect camera and classified the hand gesture into its correct symbol. Method: We used Kinect camera not the ordinary web camera because the ordinary camera does not capture its 3d orientation or depth of an image from camera however Kinect camera can capture 3d image and this will make classification more accurate. Result: Kinect camera will produce a different image for hand gestures for ‘2’ and ‘V’ and similarly for ‘1’ and ‘I’ however, normal web camera will not be able to distinguish between these two. We used hand gesture for Indian sign language and our dataset had 46339, RGB images and 46339 depth images. 80% of the total images were used for training and the remaining 20% for testing. In total 36 hand gestures were considered to capture alphabets and alphabets from A-Z and 10 for numeric, 26 for digits from 0-9 were considered to capture alphabets and Keywords. Conclusion: Along with real-time implementation, we have also shown the comparison of the performance of the various machine learning models in which we have found out the accuracy of CNN on depth- images has given the most accurate performance than other models. All these resulted were obtained on PYNQ Z2 board.

scholarly journals 3D Avatar Approach for Continuous Sign Movement Using Speech/Text

2021 ◽  
Vol 11 (8) ◽  
pp. 3439
Author(s):  
Debashis Das Chakladar ◽  
Pradeep Kumar ◽  
Shubham Mandal ◽  
Partha Pratim Roy ◽  
Masakazu Iwamura ◽  
...  
Keyword(s):  
Language Learning ◽  
Sign Language ◽  
Language Processing ◽  
Hearing Impaired ◽  
Learning System ◽  
English Sentence ◽  
Impaired People ◽  
Indian Sign Language ◽  
Impaired Person ◽  
3D Avatar

Sign language is a visual language for communication used by hearing-impaired people with the help of hand and finger movements. Indian Sign Language (ISL) is a well-developed and standard way of communication for hearing-impaired people living in India. However, other people who use spoken language always face difficulty while communicating with a hearing-impaired person due to lack of sign language knowledge. In this study, we have developed a 3D avatar-based sign language learning system that converts the input speech/text into corresponding sign movements for ISL. The system consists of three modules. Initially, the input speech is converted into an English sentence. Then, that English sentence is converted into the corresponding ISL sentence using the Natural Language Processing (NLP) technique. Finally, the motion of the 3D avatar is defined based on the ISL sentence. The translation module achieves a 10.50 SER (Sign Error Rate) score.

A Sign Language Teaching System Using Sign Language Recognition and Generation Methods

2011 ◽  
pp. 1637-1654
Author(s):  
Hirohiko Sagawa ◽  
Masaru Takeuchi
Keyword(s):  
Sign Language ◽  
Facial Expressions ◽  
Language Teaching ◽  
Head Movements ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Learning Materials ◽  
Teaching System ◽  
Lack Of Information ◽  
Impaired People

We have developed a sign language teaching system that uses sign language recognition and generation methods to overcome three problems with current learning materials: a lack of information about non-manual gestures (facial expressions, glances, head movements, etc.), display of gestures from only one or two points of view, and a lack of feedback about the correctness of the learner’s gestures. Experimental evaluation by 24 non-hearing-impaired people demonstrated that the system is effective for learning sign language.

scholarly journals Digital Sign Language App for Deaf Children

2021 ◽  
pp. 564-567
Author(s):  
Akib Khan ◽  
Rohan Bagde ◽  
Arish Sheikh ◽  
Asrar Ahmed ◽  
Rahib Khan ◽  
...  
Keyword(s):  
Bilingual Education ◽  
Sign Language ◽  
Hearing Impaired ◽  
Deaf Children ◽  
Local Language ◽  
The World ◽  
Impaired People ◽  
Inclusive Society

India has the highest population of hearing-impaired people in the world, numbering 18 million. Only 0.25 per cent of these numbers presently have access to bilingual education where knowledge of sign language is primary and that of a local language. Providing a platform that will help the entire hearing impaired community, their interpreters, families, and educators to understand sign language, will go a long way to facilitate easier conversations and exchange of ideas, thereby enabling a more inclusive society.

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 Interpretation and Translation of American Sign Language for Hearing Impaired Individuals using Image Processing

2019 ◽  
Vol 8 (4) ◽  
pp. 415-420
Keyword(s):  
Image Processing ◽  
American Sign Language ◽  
Sign Language ◽  
English Language ◽  
Hearing Impaired ◽  
Hand Gesture ◽  
American Sign ◽  
The Past ◽  
Impaired People ◽  
Live Image

The growth of technology has influenced development in various fields. Technology has helped people achieve their dreams over the past years. One such field that technology involves is aiding the hearing and speech impaired people. The obstruction between common individuals and individuals with hearing and language incapacities can be resolved by using the current technology to develop an environment such that the aforementioned easily communicate among one and other. ASL Interpreter aims to facilitate communication among the hearing and speech impaired individuals. This project mainly focuses on the development of software that can convert American Sign Language to Communicative English Language and vice-versa. This is accomplished via Image-Processing. The latter is a system that does a few activities on a picture, to acquire an improved picture or to extricate some valuable data from it. Image processing in this project is done by using MATLAB, software by MathWorks. The latter is programmed in a way that it captures the live image of the hand gesture. The captured gestures are put under the spotlight by being distinctively colored in contrast with the black background. The contrasted hand gesture will be delivered in the database as a binary equivalent of the location of each pixel and the interpreter would now link the binary value to its equivalent translation delivered in the database. This database shall be integrated into the mainframe image processing interface. The Image Processing toolbox, which is an inbuilt toolkit provided by MATLAB is used in the development of the software and Histogramic equivalents of the images are brought to the database and the extracted image will be converted to a histogram using the ‘imhist()’ function and would be compared with the same. The concluding phase of the project i.e. translation of speech to sign language is designed by matching the letter equivalent to the hand gesture in the database and displaying the result as images. The software will use a webcam to capture the hand gesture made by the user. This venture plans to facilitate the way toward learning gesture-based communication and supports hearing-impaired people to converse without trouble.

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