Because of the impediment there is a communication gap between the vocally handicapped people and the typical individuals. Around 9,000 million individuals are vocally and hearing impaired. With a population of around 7.8 billion today communication is a strong means for understanding each other. Our model has achieved a remarkable accuracy of above 90%. We are able to recognise 10 American Sign gesture alphabets with high accuracy. We make use of Convolutional Neural Network(CNN) for training and to classify the images. The features extracted are the binary pixels of the images. And the region of interest which, in our case is the hand gesture is segmented.
SIGNAL LANGUAGE SERIES
The images undergo a series of processing steps which include various Computer vision techniques such as the conversion to grayscale, dilation and mask operation. We use the HSV colour algorithm to detect the hand gesture and set the background to black. In this study, the user must be able to capture images of the hand gesture using web camera and the system shall predict and display the name of the captured image. In this paper, we introduce a Sign Language recognition using American Sign Language. However, this communication gap which has existed for years can now be narrowed with the introduction of various techniques to automate the detection of sign gestures.
There are many strings attached to this boon.The sign gestures often get mixed and confused for someone who has never learnt it or knows it in a different language. However, the invention of sign language alone, is not enough. It makes the integration process between them and others smooth and less complex. Sign language has indelibly become the ultimate panacea and is a very powerful tool for individuals with hearing and speech disability to communicate their feelings and opinions to the world. Conversing to a person with hearing disability is always a major challenge.
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