Trajectory-Based Recognition of Dynamic Persian Sign Language Using Hidden Markov Model
This work addresses the communication gap for deaf Persian speakers by enabling recognition of dynamic signs, though it is incremental as it applies existing HMM methods to a new dataset.
The study tackled the problem of recognizing dynamic Persian sign language, which is underrepresented compared to static signs, by developing a system using Hidden Markov Models on hand trajectories and shapes extracted from videos, achieving an average accuracy of 97.48% in signer-independent and signer-dependent experiments.
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.