Sign Language Recognition Techniques- A Review

Sign language reduces the barrier for communicating with the humans having impaired of speech and hearing, on the other hand Sign language cannot be easily understood by common people. Therefore, a platform is necessary that is built using an algorithm to recognize various signs it is called as Sign Language Recognition (SLR). It is a technique that simplifies the communication between speech and hearing impaired people with normal people, the main aim of SLR is to overcome the aforementioned drawback. In this manuscript it is aimed to review various techniques that have been employed in the recent past for SLR that are employed at various stages of recognition. Adding to the above, various image based with or without the glove employed for detection, their advantages and difficulties encountered during this process. Also, segmentation, feature extraction, methods used for feature vector quantization and reduction techniques are discussed in detail. Along with these, during classification it involves training, testing that employs various training models, including Hidden Markov Model based approaches and Deep learning methods such as CNN, also techniques like k-NN, ANN, SVM and others. Then finally discussion on results and observations from several techniques are compared. The approaches that are being reviewed are so flexible that they can be employed for major sign detections with applications in various domains.