Article: Identification of Tamil Sign Language using Hamiltonian deep neural network Journal: International Journal of Systems, Control and Communications (IJSCC) 2025 Vol.16 No.1 pp.62 - 78 Abstract: Sign language consisting of various hand patterns, serves as a vital communication medium for conveying messages, sharing knowledge, and expressing ideas among the deaf community. This research addresses the challenge of recognising Tamil Sign Language (TSL) by proposing a novel identification method using a Hamiltonian deep neural network (HDNN). The primary objective is to maximise the accuracy and reliability of TSL recognition, especially for real-time applications. The dataset comprises real-time images of 12 vowels, 1 Aayutha Ezhuthu, and 18 consonants, collected from 110 different signers. To address noise in the images, an iterative guided filtering (IGF) method is employed for preprocessing. Subsequently, feature extraction is performed using residual exemplars local binary pattern (RELBP). HDNN is then applied to classify the signs. The performance of the proposed approach achieves 23.32%, 25.07%, 21%, 27.53%, and 30% higher accuracy and 14.09%, 19.63%, 28%, 18.45%, and 10.54% lower error rates compared to existing methods. Inderscience Publishers - linking academia, business and industry through research

Title: Identification of Tamil Sign Language using Hamiltonian deep neural network

Authors: Ponnusamy Lingeswari; Daniel Pavunraj; Mariappan Kanagavalli; Karuppasamy Manikandan

Addresses: Department of Electronics and Communication Engineering, P.S.R. Engineering College, Sivakasi, Tamil Nadu, India ' Department of Artificial Intelligence and Data Science, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu, India ' Department of Electronics and Communication Engineering, P.S.R.R. College of Engineering, Sivakasi, Tamil Nadu, India ' Department of Computer Science and Engineering, P.S.R. Engineering College, Sivakasi, Tamil Nadu, India

Abstract: Sign language consisting of various hand patterns, serves as a vital communication medium for conveying messages, sharing knowledge, and expressing ideas among the deaf community. This research addresses the challenge of recognising Tamil Sign Language (TSL) by proposing a novel identification method using a Hamiltonian deep neural network (HDNN). The primary objective is to maximise the accuracy and reliability of TSL recognition, especially for real-time applications. The dataset comprises real-time images of 12 vowels, 1 Aayutha Ezhuthu, and 18 consonants, collected from 110 different signers. To address noise in the images, an iterative guided filtering (IGF) method is employed for preprocessing. Subsequently, feature extraction is performed using residual exemplars local binary pattern (RELBP). HDNN is then applied to classify the signs. The performance of the proposed approach achieves 23.32%, 25.07%, 21%, 27.53%, and 30% higher accuracy and 14.09%, 19.63%, 28%, 18.45%, and 10.54% lower error rates compared to existing methods.

Keywords: Tamil Sign Language recognition; Hamiltonian deep neural network; HDNN; iterative guided filtering; IGF; residual exemplars local binary pattern; RELBP.

DOI: 10.1504/IJSCC.2025.144542

International Journal of Systems, Control and Communications, 2025 Vol.16 No.1, pp.62 - 78

Received: 29 May 2024
Accepted: 13 Nov 2024
Published online: 18 Feb 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Identification of Tamil Sign Language using Hamiltonian deep neural network

Keep up-to-date