Top Research Papers on Sign Language Recognition

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.

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.

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 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.

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 Natural Language-Assisted Sign Language Recognition (NLA-SLR) framework is proposed, which exploits semantic information contained in glosses (sign labels) and introduces a novel backbone, video-keypoint network, which not only models both RGB videos and human body keypoints but also derives knowledge from sign videos of different temporal receptive fields.

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.

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.

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).

The proposed model has been trained and tested on video clips of dynamic signs and achieved the training accuracy of 70%.

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.

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 prototype sign language interpreter that can converse in American Sign Language (ASL) is presented in this study and the suggested system recognizes letters, numbers, and certain common words in sign language.

This work is using Deep Learning Models to enhancing Realtime sign detection and recognition and it helps the deaf and dumb people to connect with the world.

The feasibility of utilizing multilingual sign language corpora to facilitate monolingual CSLR is explored, and the underlying idea is to identify the cross-lingual signs in one sign language and properly leverage them as auxiliary training data to improve the recognition capability of another.

MediaPipe is used as a method of feature extraction to extract coordinates of the joint points of the human body and is able to recognize 64 Argentine sign language words or phrases.

This work proposes a deep learning-based algorithm that can identify words from a person’s gestures and detect them using a hybrid approach that achieves around 92% accuracy for different continuous signs, and the YOLOv6 model achieves 96% accuracy over different static signs.

The goal of creating this dataset was to provide a robust foundation for further research in NSL recognition, comprising 24,300 images of 27 NSL letters captured under varying conditions to represent each sign comprehensively.

A sign detector is developed that can detect signs and numbers and also other signs that are used in sign language with the help of OpenCV and Keras modules in python to recognize sign languages for hearing-impaired persons.

CorrNet is proposed to explicitly capture and leverage body trajectories across frames to identify signs in continuous sign language recognition and achieves new state-of-the-art accuracy on four largescale datasets, i.e., PHOenIX14, PHOENIX14-T, CSL-Daily, and CSL.

The Transformer Encoder is proposed as a useful tool for sign language recognition and shows noticeable performance over other state-of-the-art convolution architecture.

The motivation behind Sign-Speak is presented, outlining the pressing need for effective communication tools for the mute community, and the technical aspects of the project, its implementation details, and the potential impact on enhancing communication accessibility for mute individuals are discussed.

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.

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.

This research proposes a method to bridge the communication gap between hearing impaired people and others, which translates signed gestures into text, using signal processing and supervised learning based on a vocabulary of 49 words and 346 sentences.

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.

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.

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.

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.

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.

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 study looked at the vision-based sign language recognition models suggested utilizing deep learning approaches in the last five years and revealed that sign language Recognition accuracy has substantially increased, but specific problems still have to be overcome.

This research explains an overview of Sign Language, gestures, and the most important techniques used to distinguish Sign Language.

This paper provides a brief survey of various research works carried out so far in the field of sign language technology.

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.

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.

This paper introduces a robust pose normalization scheme which takes the signing space in consideration and processes the hand poses in a separate local coordinate system, independent on the body pose, and demonstrates the significant impact of this normalization on the accuracy of the proposed system.

This paper introduces the idea of exploiting the phonological properties manually assigned by sign language users to classify videos of people performing signs by regressing a 3D mesh and establishes a new baseline for this problem based on the statistical distribution of 725 different signs.

A prototype system that helps to recognize hand gesture to normal people in order to communicate more effectively with the special people as well as with the community of deaf people is presented.

This research presentsings to recognize American Sign Language from series of hand gestures using DenseNet201, LSTM and applies transfer learning models in combination with deep neural networks and background subtraction for videos in different temporal settings.

This research looks at how well an algorithm can be trained to spot certain mouthing points and output the mouth annotations with a high degree of accuracy, so that the appropriate mOUthing for animated signs can be easily applied to avatar technologies.

Assessment of ML-based SLR model was conducted with the help of 4 candidates under a controlled environment, and the model yielded 65% accuracy.

The report summarizes the basic concepts and methods in creating this android application that uses gestures recognition to understand American sign language words and uses pattern recognition techniques for gesture recognition.

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 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.

Conversing with an individual along with listening to impairment is consistently a primary problem and the interaction void, which has actually existed for many years, may currently be tightened along with the introduction of numerous methods to automate the discovery of authorized motions.

This paper shows that textual and attribute based class definitions can provide effective knowledge for the recognition of previously unseen sign classes and introduces techniques to analyze the influence of binary attributes in correct and incorrect zero-shot predictions.