Recognition of Sign Language from High Resolution Images Using Adaptive Feature Extraction and Classification

Recognition of Sign Language from High Resolution Images Using Adaptive Feature Extraction and Classification

Blog Article

A variety of algorithms allows gesture recognition in video sequences.Alleviating the need for interpreters is of interest to hearing impaired people, since it allows a great degree of self-sufficiency in communicating their intent to the non-sign language speakers without the need for Photo Frame interpreters.State-of-theart in currently used algorithms in this domain is capable of either real-time recognition of sign language in low resolution videos or non-real-time recognition in high-resolution videos.

This paper proposes a novel approach to real-time recognition of fingerspelling alphabet letters of American Sign Language (ASL) in ultra-high-resolution (UHD) video sequences.The proposed Chromium approach is based on adaptive Laplacian of Gaussian (LoG) filtering with local extrema detection using Features from Accelerated Segment Test (FAST) algorithm classified by a Convolutional Neural Network (CNN).The recognition rate of our algorithm was verified on real-life data.