Trajectory-Based Recognition of Dynamic Persian Sign Language Using Hidden Markov Model

TL;DR

This paper presents a dynamic Persian Sign Language recognition system using hand trajectory modeling with Hidden Markov Models, achieving high accuracy and robustness across different signers and limited training data.

Contribution

The study introduces a novel dynamic sign language recognition approach using hand trajectories and HMMs, applicable to Persian Sign Language.

Findings

Achieved 97.48% average accuracy in recognition.

System performs well with limited training data.

Performance is signer-independent and robust.

Abstract

Sign Language Recognition (SLR) is an important step in facilitating the communication among deaf people and the rest of society. Existing Persian sign language recognition systems are mainly restricted to static signs which are not very useful in everyday communications. In this study, a dynamic Persian sign language recognition system is presented. A collection of 1200 videos were captured from 12 individuals performing 20 dynamic signs with a simple white glove. The trajectory of the hands, along with hand shape information were extracted from each video using a simple region-growing technique. These time-varying trajectories were then modeled using Hidden Markov Model (HMM) with Gaussian probability density functions as observations. The performance of the system was evaluated in different experimental strategies. Signer-independent and signer-dependent experiments were performed on the proposed system and the average accuracy of 97.48% was obtained. The experimental results demonstrated that the performance of the system is independent of the subject and it can also perform excellently even with a limited number of training data.