Development of benchmark datasets of multioriented hand gestures for speech and hearing disabled

Reliable hand gesture recognition is extremely relevant for automatic interpretation of sign languages used by people with hearing and speech disabilities. In this work, we present (i) new benchmark datasets of depth-sensor based, multi-oriented, isolated and static hand gestures of numerals and alphabets following the conventions of American Sign Language (ASL), (ii) an effective strategy for segmentation of hand region from depth data and appropriate preprocessing for feature extraction, and (iii) an effective statistical-geometrical feature set for recognition of multi-oriented hand gestures. Besides setting benchmark performances on the developed datasets, viz. 97.67%, 96.53% and 96.86% on numerals, alphabets and alpha-numerals respectively, the proposed pipeline is also implemented on two related public datasets and is found superior to state-of-the-art methods reported so far.