Sign Language Recognition using Smart Glove

Gesture-based communication Recognition through innovation has been ignored idea even though an enormous local area can profit from it. There are more than 3% total population of the world who even can't speak or hear properly. Using hand Gesture-based communication, Every especially impaired people to communicate with each other or rest of world population. It is a way of correspondence for the rest of the world population who are neither speaking and hearing incompetency. Normal people even don't become intimate or close with sign language based communications. It's a reason become a gap between the especially impaired people & ordinary person. The previous systems of the project used to involve the concepts of image generation and emoji symbols. But the previous frameworks of a project are not affordable and not portable for the impaired person.The Main propaganda of a project has always been to interpret Indian Sign Language Standards and American Sign Language Standards and Convert gestures into voice and text, also assist the impaired person can interact with the other person from the remote location. This hand smart glove has been made with the set up with Gyroscope, Flex Sensor, ESP32 Microcontrollers/Micro bit, Accelerometer,25 LED Matrix Actuators/Output &, flex sensor, vibrator etc.

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Indian Sign Language Recognition on PYNQ Board

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200909110140 ◽

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.

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

Sensors ◽

10.3390/s20216256 ◽

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.

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Sign Language Recognition

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c4565.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 2128-2137

Keyword(s):

American Sign Language ◽

Sign Language ◽

Hand Gesture ◽

Language Recognition ◽

Sign Language Recognition ◽

Normal People ◽

The People ◽

The World ◽

The Disabled ◽

Communication Barrier

There are nearly 15 million people around the world who have difficulty in speaking or communicating. Their only way of communication is through sign language. Hand gesture is one of the methods used in sign language for non-verbal communication. It is most commonly used by deaf & dumb people who have hearing or speech problems to communicate among themselves or with normal people. There are many recognized sign language standards that have been defined such as ASL(American Sign Language), IPSL(Indo Pakistan Sign Language), etc., which define what sign means what. ASL is the most widely used sign language by the deaf and dumb community. The deaf and dumb use sign language to communicate among themselves with the knowledge of the standard sign language. But they can’t communicate with the rest of the world as most of the people are unaware of the existence and the usage of the sign language. This method aims to remove this communication barrier between the disabled and the rest of the world by recognizing and translating the hand gestures and convert it into speech

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