International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 11 | Nov 2022
p-ISSN: 2395-0072
www.irjet.net
Sign Language Recognition Aishwarya L Student, Department of Information Technology, Bannari Amman Institute Of Technology, Erode, Tamil Nadu, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The biggest challenge with sign language is not
(hand) from non-pixels (background). The images were fed into the model called the Convolutional Neural Network (CNN) for the classification of images. Keras was used for the training of images. Provided with proper lighting conditions and uniform background, the system acquired an average testing accuracy of 93.67%, of which 90.04% was attributed to ASL alphabet recognition, 93.44% for number recognition and 97.52% for static word recognition, thus surpassing that of other related studies. The approach is used for fast computation and is done in real-time. The hidden Markov model (HMM) works on continuous SLR because HMM enables the segmentation of data stream into its continuous signs implicitly, thus bypassing the hard problem of segmentation entirely.
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.
3. PROBLEM DEFINITION
Key Words: Datasets, sign language, Communication,
One of the important thing in social survival is communication. Communicating with the person with hearing disability is a challenging one. Deaf and dumb people use combination of hand movements, hand shapes in order to convey particular information. Normal people face difficulty in understanding their language. Sign language is a message behind the hand. So, there is a need for a system that recognizes the different signs, gestures and conveys the information to normal people. It bridges the gap between physically challenged people and normal people.
Deaf, Dumb, Indian sign language
1. INTRODUCTION As stipulated by Nelson Mandela, Talk to a man in a language he understands, that goes to his head. Talk to him in his own language, that goes to his heart, language is undoubtedly essential to human interaction and has existed since human civilization began. It is the medium people use to communicate and express themselves to the real world. Sign language is the primary means of communication in the deaf and dumb community. Only a minimal number of people are aware of Sign language and so there is a need for a system that recognizes alphanumeric gestures. Through various researches, efforts are made to build system for the problem predominantly for ASL gesture detection and a minuscule amount of focus has been given to ISL gestures.
4. PROPOSED SYSTEM To solve the problem of communication between a normal person and who was lacking to speak the word or hearing the person’s voice, we build our proposed system in simple ways. We create the sign detector which will detect the numbers from 1 to 10 and alphabets A to Z. To identify the hand gestures of the person we divided our proposed system into 3 phases.
2. LITERATURE REVIEW L. G. Zhang, Y. Chen, G. Fang, X. Chen, and W. Gao, “A visionbased sign language recognition system using tied-mixture density HMM” , in ICMI '04: Proceedings of the 6th international conference on Multimodal interfaces, 2004, pp 198–204, doi:10.11451027933.1027967
5. SYSTEM REQUIREMENTS 5.1.Software Requirements
In this paper, a system was developed that will serve as a learning tool for starters in sign language that involves hand detection. This system is based on a skin-color modeling technique, i.e., explicit skin-color space thresholding. The skin-color range is predetermined that will extract pixels
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