Image-based Indian Sign Language Recognition: A Practical Review using Deep Neural Networks

• URL: http://arxiv.org/abs/2304.14710v1
• Date: Fri, 28 Apr 2023 09:27:04 GMT
• Title: Image-based Indian Sign Language Recognition: A Practical Review using Deep Neural Networks
• Authors: Mallikharjuna Rao K, Harleen Kaur, Sanjam Kaur Bedi, and M A Lekhana
• Abstract summary: This model is to develop a real-time word-level sign language recognition system that would translate sign language to text. For this analysis, the user must be able to take pictures of hand movements using a web camera. Our model is trained using a convolutional neural network (CNN), which is then utilized to recognize the images.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: People with vocal and hearing disabilities use sign language to express themselves using visual gestures and signs. Although sign language is a solution for communication difficulties faced by deaf people, there are still problems as most of the general population cannot understand this language, creating a communication barrier, especially in places such as banks, airports, supermarkets, etc. [1]. A sign language recognition(SLR) system is a must to solve this problem. The main focus of this model is to develop a real-time word-level sign language recognition system that would translate sign language to text. Much research has been done on ASL(American sign language). Thus, we have worked on ISL(Indian sign language) to cater to the needs of the deaf and hard-of-hearing community of India[2]. In this research, we provide an Indian Sign Language-based Sign Language recognition system. For this analysis, the user must be able to take pictures of hand movements using a web camera, and the system must anticipate and display the name of the taken picture. The acquired image goes through several processing phases, some of which use computer vision techniques, including grayscale conversion, dilatation, and masking. Our model is trained using a convolutional neural network (CNN), which is then utilized to recognize the images. Our best model has a 99% accuracy rate[3].

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