Towards a SignWriting recognition system

2020

Major development is being done in research for Sign Language Recognition. A lot of techniques has been developed in this field. The Sign Language is used essentially for deaf and dumb people to communicate with each other. This paper shows the sign language recognizing of 28 including backspace and whitespace hand gestures in American sign language. The proposed system contains five modules such as: Setting up the model, caching bottlenecks, adding FC layer to the model, training the model and writing about trained graphs and labels.

The biggest challenge with sign language is not being universal because it varies from country to country. Sign language is a way of communication adopted by hearing and speech impaired people by using hand gestures. It is a challenge for other people to communicate with them and vice versa. To make communication easier, there is a need for a bridge connecting the gap between physically challenged people and others. This project focuses on identifying the characters and numbers of Indian Sign Language using Convolutional Neural Network (CNN), Keras, Tensor Flow libraries. India doesn't have standard sign language but adopts ASL (American Sign Language), which is single-handed whereas ISL uses two hands for communicating. These reasons boosted us to develop software recognizing hand gestures using ISL.

nternational journal of advanced research in computer and communication engineering, 2023

Communication is a crucial factor of human interaction. Due to our hearing ability, we can understand thoughts of each other. But what if one absolutely cannot hear anything and eventually cannot speak? So Sign Language is the main communicating tool for hearing impaired and mute people, and also to ensure an independent life for them. This paper proposes a system to recognize the hand gestures using a Deep Learning Algorithm, Convolution Neural Network (CNN) to process the image and predict the gestures. This paper shows the sign language recognition of 26 alphabets and 0-9 digits hand gestures of American Sign Language as well as some other general words. The proposed system contains modules such as image pre-processing and feature extraction, training and testing of model and sign to text and audio conversion. Different CNN architecture and pre-processing techniques such as greyscale, thresholding, skin masking, and Canny Edge Detection were designed and tested with our dataset to obtain better accuracy.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

A large number of deaf and mute people are present around the world and communicating with them is a bit difficult at times; because not everyone can understand Sign language(a system of communication using visual gestures and signs). In addition, there is a lack of official sign language interpreters. In India, the official number of approved sign language interpreters is only 250[1]. This makes communication with deaf and mute people very difficult. The majority of deaf and dumb teaching methods involve accommodating them to people who do not have disabilities-while discouraging the use of sign language. There is a need to encourage the use of sign language. People communicate with each other in sign language by using hand and finger gestures. The language serves its purpose by bridging the gap between the deaf-mute and speaking communities. With recent technological developments, sign language identification is a hard subject in the field of computer vision that has room for further progress. In this project, we propose 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. Using Neural Networks, we offer a machine learning-based technique for identifying American Sign Language.

Communication through signs has consistently been a significant way for communication among hearing and speech impaired humans, generally called deaf and dumb. It is the only mode of communicating for such individuals to pass on their messages to other human beings, and hence other humans need to comprehend their language. In this project, a sign language detection or recognition web framework is proposed with the help of image processing. This application would help in recognizing Sign Language. The dataset used is the Indian Sign Language dataset. This application could be used in schools or any place, which would make the communication process easier between the impaired and nonimpaired people. The proposed method can be used for the ease of recognition of sign language. The method used is Deep Learning for image recognition and the data is trained using Convolution Neural Network. Using this method, we would recognize the gesture and predict which sign is shown.

IRJET, 2022

Normal human beings can easily interact and communicate with each other but a person with hearing and speaking disabilities while communicating with normal people without a translator. Sign language is a visual language used by the people with the speaking and hearing disabilities for communication in their daily conversation activities. This is the reason that the implementation of a system that recognize the sign language would have a relevant benefit impact on dumb-deaf people. Here the sign gestures or text would be the input and corresponding description or signs would be the output respectively. In the proposed approach the main focus is on the classification and recognition of the Indian Sign Language given by the user in real-time. For classification, CNN and neural network are used.

international journal of engineering technology and management sciences

Sign language is a system for communication using visual gestures and signs for deaf and dumb people. This language is especially used by people who can’t speak or hear. Sign language is oldest and natural form of language for communication, since most of the people are not aware of sign language, hence it is tedious to understand, for solving this problem we have built a real time method using convolution neural networks (CNN) for hand-based gestures to detect sign language. In our CNN model, first the hand is passed through a filter and then the filter is applied. Finally, the hand is passed through a classifier which predicts the class of the hand gestures. Our model provides 98% accuracy for the alphabet A-Z letters. On the other hand, the most common hurdle deaf and dumb come across is communication with the normal people and with the fact i.e., each and every normal person doesn't know the sign language. Another main feature of the project is to create a communication syst...

International Journal of Advanced Trends in Computer Science and Engineering, 2021

Sign language is one of the oldest and most people used to communicate with people like Deaf and Dumb. Presently most people don't know sign language and it's become very tough to communicate with deaf people to clear those barriers. They created a real time live fingers spelling based application using Deep learning techniques. Gesture is the Symbol and physical emotion to the Deaf people. In this Method first hand is passed through the Filter and based on fingers it will predict the Alphabet and Display. Here I was using the VGG16 model which gives 99% accuracy for the 26 letters of the Alphabets.

Journal IJETRM , 2025

Communication barriers between individuals with hearing impairments and non-sign language users create challenges in education, healthcare, and daily interactions. To bridge this gap, this research presents an intelligent Sign Language to Text and Vice Versa Translation System, leveraging machine learning, natural language processing (NLP), and Unity 3D animation. The proposed system facilitates seamless two-way communication by recognizing sign language gestures and converting them into text, while also transforming textual input into animated sign language representations. The system's sign language recognition module employs computer vision and deep learning (CNNs) to detect and classify hand gestures in real time. The text-to-sign translation module utilizes NLP tokenization to process text and generate corresponding sign animations using a 3D avatar in Unity 3D. The integration of these technologies ensures high accuracy, real-time processing, and an interactive user experience. Experimental results demonstrate the system's effectiveness in accurately recognizing and translating sign language gestures, with significant potential for enhancing accessibility in education, public services, and digital communication platforms. By providing an efficient and scalable solution, this research contributes to the advancement of assistive technologies, fostering inclusivity for the deaf and hard-of-hearing community.

Deaf and hard-of-hearing persons, as well as others who are unable to communicate verbally, utilise sign language to communicate within their communities and with others. Sign languages are a set of preset languages that communicate information using a visual-manual modality. The dilemma of real-time finger-spelling recognition in Sign Language is discussed. We gathered a dataset for identifying 36 distinct gestures (alphabets and numerals) and a dataset for typical hand gestures in ISL created from scratch using webcam images. The system accepts a hand gesture as input and displays the identified character on the monitor screen in real time. This project falls under the category of human-computer interaction (HCI) and tries to recognise multiple alphabets (a-z), digits (0-9) and several typical ISL hand gestures. To apply Transfer learning to the problem, we used a Pre-Trained SSD Mobile net V2 architecture trained on our own dataset. In the vast majority of situations, we constructed a robust model that consistently classifies Sign language. Many studies have been done in the past in this area employing sensors (such as glove sensors) and other image processing techniques (such as edge detection technique, Hough Transform, and so on), but these technologies are quite costly, and many people cannot afford them. During the study, various human-computer interaction approaches for posture recognition were investigated and evaluated. The optimum solution was determined to comprise a set of image processing approaches with Human movement categorization. Without a controlled background and low light, the system can detect chosen Sign Language signs with an accuracy of 70-80%. As a result, we're creating this software to assist such folks because it's free and simple to use. However, aside from a small group of people, not everyone is familiar with sign language, and they may need an interpreter, which may be cumbersome and costly. This research intends to bridge the communication gap by building algorithms that can anticipate alphanumeric hand motions in sign language in real time. The main goal of this research is to create a computer-based intelligent system that will allow deaf persons to interact effectively with others by utilising hand gestures.