Top Research Papers on Sign Language Recognition

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.

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.

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.

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 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.

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.

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.

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 is a vital form of communication for the deaf and hard-of-hearing community, playing a crucial role in fostering social interaction, education, and access to essential services. However, a significant barrier exists between sign language users and non-signers, often leading to misunderstandings and reduced accessibility in various contexts. To address this challenge, we propose a novel real-time sign language detection system that utilizes standard web cameras, aiming to bridge the communication gap effectively. This innovative system is designed to recognize sign language gestur...

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.

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 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.

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.

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 paper proposes a framework to address the lack of standardization at format level, unify the available resources and facilitate SL research for different languages, and presents a proof of concept, training neural translation models on the data produced by the proposed framework.

— Deaf and mute people communicate via hand gestures, i

The project aims at bridging the communication gap with voice and hearing-impaired people, helping them to converse with the world more fluently using hand gestures as the primary input and converts those into understandable language.

This thesis aims to recognize sign language and focus specially on the gestures performed by the deaf and dumb persons in a multi-modal context and its utility is justified by the large number of the targeted population.

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.

The main objective of this idea is to convert the Sign Language into a human-readable and understandable format which is text and speech.

A system that can recognize poses and hand gestures of Indian Sign Language in real time in real time using grid-based features to reduce the communication gap between listening and speaking disabled and the rest of society is introduced.

A real-time method using neural networks for fingerspelling-based Indian Sign Language for classifying 36 different gestures (alphabets and numerals) using Convolutional Neural Network.

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.

The use of deep learning for sign language recognition is discussed, where the model will learn to detect the hand motions images throughout an epoch, using Deep Learning Computer Vision to recognize the hand gestures.

Various ways a sign language recognition system has been built or has been proposed by different researchers are described and various methods of recognizing and predicting the hand signs/hand gestures of the specially able people of the society are described.

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.

This article provides an analytical overview of the different types of explicit legal recognition of sign languages.

The objective of the research presented in this paper is to facilitate the communication between the deaf and non deaf people and to achieve this goal, computers should be able to visually recognize hand gestures from image input.

Various methods and techniques for recognizing text signatures in images and compares different methods and algorithms with the help of a pie chart are described.

A dataset is gathered and several feature extraction methodologies are used to retrieve important data, which is then input into supervised learning methods in this study to take a step forward in this field.

A Sign Language Recognition System designed to facilitate seamless communication between individuals proficient in sign language and those who may not share this proficiency, integrating computer vision, machine learning, and signal processing techniques to accurately interpret and recognize sign language gestures.

Outre les gestes de la main, la langue des signes utilise simultanement differents composants pour transmettre un message. A titre d’exemple, l’orientation des doigts, les mouvements des bras ou du corps ainsi que les expressions faciales. Parfois, un composant specifique peut jouer un role majeur dans la modification de la signification du signe ou peut ne pas etre requis pour interpreter un signe. Pour cela, il est primordial pour un systeme de reconnaissance de n’utiliser que les informations pertinentes pour traduire un signe. Dans ce contexte, nous avons elabore le Sign Transformer Networ...

This paper explains two way means of communication between impaired and normal people which implies that the proposed ideology can convert sign language to text and voice.

This paper proposes a model that combines MediaPipe Holistic with a neural network, such as SimpleRNN, LSTM, or GRU, to recognize Makaton sign language (MSL).

It is proposed that number of finger tips and the distance of fingertips from the centroid of the hand can be used along with PCA for robustness and efficient results and recognition with neural networks is proposed.

This paper is based on the survey of the current research trends in the field of SL recognition to highlight the current status of different research aspects of the area and critically analyzed the current research to identify the problem areas and challenges faced by the researchers.

This paper is based on the survey of the current research trends in the field of SL recognition to highlight the current status of different research aspects of the area and critically analyzed the current research to identify the problem areas and challenges faced by the researchers.

This project mainly deals with making services of the hand gestures to be easily accessible and understandable to by the people using sign languages, and is built using machine learning tools, TensorFlow library.

This work uses Deep Learning Computer Vision to recognize the hand gestures for sign language by building Deep Neural Network architectures (Convolution Neural Network Architectures) using Deep Neural Network architectures.

The objective of this system is to expedite the recognition and classification of sign gestures, providing a quick and reliable non-invasive solution for users and facilitating communication.

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.

The basic idea of this project is to make a system using which dumb people can significantly communicate with all other people using their normal gestures, and it does not require the background to be perfectly black.

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.

A Semi-Continuous Hidden Markov Model for sign language recognition is presented, shows that SCHMM is prior to the DHMM and the CHMM in theory and debase the complexity and the operations at the same recognition rate.

An Automatic translation system for gesture of manual alphabets in Marathi sign language and system and methods for the automatic recognition of Marathi sign language are presented.

An intelligent glove has been designed to automate the communication between a deaf-mute with others by converting sign language into speech or understandable language and has been observed that the support vector machine has the highest accuracy.

A novel system to recognize Indian Sign Language (ISL) in Real-Time through the mobile application that will bridge the communication gap between the hearing, speech impaired and the rest of the society.

A real-time method using neural networks for fingerspelling-based Indian Sign Language for classifying 36 different gestures (alphabets and numerals) using Convolutional Neural Network.