International Journal of Innovative Research and Development

Abstract

Sign language is a language which uses visually transmitted sign patterns to convey meaning. It is the combination of hand shapes, orientation and movement of hands, arms or body, and facial expressions. Our System is capable of recognizing sign-language symbols can be used as a means of communication with hard of hearing people. Our paper proposes a system to help normal people can easily communicate with hard of hearing people. Instead we are using a camera and microphone as a device to implement the Indian Sign Language (ISL) system. The ISL translation system has translation of voice into Indian Sign Language. The ISL translation system uses microphone or USB camera to get images or continuous video image (from normal people) which can be interpreted by the application. Acquired voices are assumed to be translation, scale and rotation invariant. In this process the steps of translation are acquisition of images, binarized, classification, hand shape edge detection and feature extraction. After getting vectors feature extraction state then pattern matching done by comparing existing database. The GUI application is displaying and sending the message to the receiver. This system makes normal people to communicate easily with deaf/dumb person. Also in video calling or chatting this application helps the hard speaking and hearing people.

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Abstract

The need of computer recognition sign language image processing is essential for hearing impaired people. Preprocessing is very much required task to be done in hand gesture recognition system. In preprocessing process, the segmentation is an important part to detect the hand sign. The faculty of vision based gesture recognition to be a natural, powerful, and friendly tool for supporting efficient interaction between human and machine. In this paper new hybrid image segmentation is provided to detect the hand sign language image based on canny edge detection method and fuzzy c means clustering with thresholding technique. Here canny edge detection is applied to extract the finger tips of hand sign image accurately. Followed by fuzzy c means clustering method for final tuning of segmented image with better image quality of index. The   new method is validated with the parameters in terms of energy level, Entropy level, and Evaluation time (ET).  The experimental result shows that the proposed model works efficiently.

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