Top Research Papers on Sign Language Recognition

The processes needed to recognise sign language are outlined and the method of gathering data, as well as its preprocessing, transformation, feature extraction, categorization, and outcomes, are examined.

This system aims to bridge this communication gap and aid the deaf and the mute to use technology to carry out their daily transactions by using a simple approach which is easily implementable.

An improved method for sign language recognition and conversion of speech to signs is discussed and results show satisfactory segmentation of signs under diverse backgrounds and relatively high accuracy in gesture and speech recognition.

A novel real-time sign language detection system that utilizes standard web cameras, aiming to bridge the communication gap effectively and provides dual output: voice for real-time interpretation, facilitating immediate communication for those who may not be familiar with sign language, and on-screen text, which serves as a visual reference for users.

The goal of this project is to provide a Human Computer Interaction system to resolve the problem faced by the deaf and dumb people and the algorithm is not designed on the base of background hand gestures, it is immune to changes in the background image.

This paper focuses on experimenting with different segmentation approaches and unsupervised learning algorithms to create an accurate sign language recognition model and achieves a classification accuracy of 98% on a randomly selected set of test data using the trained model.

This system uses a camera, which captures various gestures of the hand, and a template-matching algorithm identifies the sign and display the text, which curtails the difficulty to communicate with the deaf.

An application to mitigate the issue between the communication of differently abled and others and reduce the dependencies on third parties like translators using a machine learning model.

The proposed algorithm provides 95 accurate alphabetical results and its image is captured at all possible angles and distances and its algorithm work accurately for 45 input types.

An image processing algorithm is presented for the interpretation of the Taiwanese sign language, which is one of the sign languages used by the majority of the deaf community and can achieve 100% recognition rate for test persons.

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.

Sign-language (SL) recognition, even though it has been under investigation for many years, remains a challenge in real practice and is used for simultaneous video sensor, based on which hand and body action can be tracked more accurately and easily.

The sign language translator uses a glove fitted sensors that can interrupt the basic words of the differently abled sign language, which will give an opportunity for physically disabled people to communicate with ordinary people.

A sign language detection or recognition web framework is proposed with the help of image processing that could be used in schools or any place, which would make the communication process easier between the impaired and non-impaired people.

A design is presented that can recognize various American sign language static hand motions in real-time using transfer learning, Python, and OpenCV and recognize “Hello, Yes, No, Thank You, and I Love You" are all prevalent sign language terms that the system correctly acknowledges.

The neural network of this system used extracted image features as input and it was trained using back-propagation algorithm to recognize which letter was the given letter with accuracy of respectively 70% and 85% with two proposed classifiers.

Sign language number recognition system lays down foundation for handshape recognition which addresses real and current problems in signing in the deaf community and leads to practical applications.

The proposed paper provides a user-friendly way of communication by using the CNN algorithm which helps the deafmute people to communicate with normal people easily.

This project proposes an optimal recognition engine whose main objective is to translate static American Sign Language alphabets, numbers, and words into human and machine understandable English script and the other way around.

Sign language is the preferred method of communication among the deaf and the hearing impaired people all over the world and can have varying degree of success when used in a computer vision or any other methods.