A Topological derivative based image segmentation for sign language recognition system using isotropic filter

TL;DR

This paper introduces a novel image segmentation method using topological derivatives combined with restoration to improve sign language recognition accuracy, addressing challenges posed by hand shape and motion variability.

Contribution

It proposes a new segmentation approach leveraging topological derivatives and restoration, enhancing hand posture detection in sign language recognition systems.

Findings

Restoration before segmentation improves hand detection accuracy.

Discrete derivatives effectively discriminate hand postures from background.

Method achieves high recognition accuracy in sign language images.

Abstract

The need of sign language is increasing radically especially to hearing impaired community. Only few research groups try to automatically recognize sign language from video, colored gloves and etc. Their approach requires a valid segmentation of the data that is used for training and of the data that is used to be recognized. Recognition of a sign language image sequence is challenging because of the variety of hand shapes and hand motions. Here, this paper proposes to apply a combination of image segmentation with restoration using topological derivatives for achieving high recognition accuracy. Image quality measures are conceded here to differentiate the methods both subjectively as well as objectively. Experiments show that the additional use of the restoration before segmenting the postures significantly improves the correct rate of hand detection, and that the discrete derivatives yields a high rate of discrimination between different static hand postures as well as between hand postures and the scene background. Eventually, the research is to contribute to the implementation of automated sign language recognition system mainly established for the welfare purpose.