Language Recognition System: An Application Based Study with Special Reference to Sociolinguistics and Computational Linguistics

The research work proposes a system which can handle different types of selected words into a common vision-based platform. These sign languages are used not only to communicate with deaf and dumb people but also to exchange ideas with people who can hear but physically cannot speak. Most of the researchers work on the sign language by processing the video with frames in an equal or regular interval. Some of the factors that affect this phenomenon are colour, angle, the position of hand-disturb, dialectical variations of languages etc. All these factors act as impediments to create a perfect sign language system. Region based analysis is used to exploit the interior pixels and boundary of the object. These shape descriptors are more sensible to distortions and noise. The sign language learning is classified into unsupervised and supervised learning. The studies in the realm of sign language can be comprehended as a complicated one and researches are going on in the fields of supervised and unsupervised learning. The research work proposes a system which can handle different types of selected words into a common vision-based platform. Feature extractions like Wavelet transform method can be used in future for better results. Other classifiers like Component Analysis, Support Vector Machine and Linear Discrimination Analysis can also be used for conducting experiments and to improve the recognition rate.