Author(s)

Sharmin Akter Milu¹, Azmath Fathima², Tanmay Talukder³, Inzamamul Islam^4*, Md. Ismail Siddiqi Emon⁵

¹Department of Computer Science & Telecommunication Engineering (CSTE), Noakhali Science & Technology University, Noakhali, Bangladesh.
²School of Computer, Mathematical & Natural Sciences (SCMNS), Morgan State University, Baltimore, MD, USA.
³Department of Physics, Morgan State University, Baltimore, MD, USA.
⁴Department of ECE, Morgan State University, Baltimore, MD, USA.
⁵Department of Computer Science, Morgan State University, Baltimore, MD, USA.

Gesture detection is the primary and most significant step for sign language detection and sign language is the communication medium for people with speaking and hearing disabilities. This paper presents a novel method for dynamic hand gesture detection using Hidden Markov Models (HMMs) where we detect different English alphabet letters by tracing hand movements. The process involves skin color-based segmentation for hand isolation in video frames, followed by morphological operations to enhance image trajectories. Our system employs hand tracking and trajectory smoothing techniques, such as the Kalman filter, to monitor hand movements and refine gesture paths. Quantized sequences are then analyzed using the Baum-Welch Re-estimation Algorithm, an HMM-based approach. A maximum likelihood classifier is used to identify the most probable letter from the test sequences. Our method demonstrates significant improvements over traditional recognition techniques in real-time, automatic hand gesture recognition, particularly in its ability to distinguish complex gestures. The experimental results confirm the effectiveness of our approach in enhancing gesture-based sign language detection to alleviate the barrier between the deaf and hard-of-hearing community and general people.

Hand Gesture Recognition System

Milu, S. , Fathima, A. , Talukder, T. , Islam, I. and Emon, M. (2024) Design and Implementation of Hand Gesture Detection System Using HM Model for Sign Language Recognition Development. Journal of Data Analysis and Information Processing, 12, 139-150. doi: 10.4236/jdaip.2024.122008.