Sign Language Recognition

This chapter covers the key aspects of sign-language recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data set

Sign Language Recognition System Simulated for Video Captured with Smart Phone Front Camera

This works objective is to bring sign language closer to real time implementation on mobile platforms. A video database of Indian sign language is created with a mobile front camera in selfie mode. This video is processed on a personal computer by constraining the computing power to that of a smart phone with 2GB ram. Pre-filtering, segmentation and feature extraction on video frames creates a sign language feature space. Minimum distance classification of the sign feature space converts signs to text or speech. ASUS smart phone with 5M pixel front camera captures continuous sign videos containing around 240 frames at a frame rate of 30fps. Sobel edge operator’s power is enhanced with morphology and adaptive thresholding giving a near perfect segmentation of hand and head portions. Word matching score (WMS) estimates performance of the proposed method with an average WMS of around 90.58%