Citation :

V. Sivakamasundari, P. Anbarasu, M. Arivazhagan, K. Rajendran, "TamilNets-V1-A Novel Resource Constraint Deep Learning Framework for Tamil Sign Language Detection Mechanism," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 6, pp. 157-172, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I6P111

Abstract

Tamil is considered one of the ancient and unique languages, enriched with distinctive texts and knowledge in various domains, including literature, healthcare, scientific devotion, agriculture, and so on. However, its rich resources remain on the darker side to the deaf and dumb community since they are facing the challenges of establishing better social interactions. Sign Language (SL) is a kind of communication that helps them to understand and increase their knowledge. Furthermore, signs, gestures, and facial expressions are used as catalysts for enhancing them to learn new skills, thereby improving their quality of life. With the advent of ML and DL, Tamil Sign Language Recognition Systems (TSLRs) have reached their new peak of design but suffer from bottlenecks such as computational complexity, high resource constraints, and consume more device power. To solve this challenge, this research article proposes a novel resource and computationally aware cognitive framework for TSLR Systems. The cognitive framework TamilNets-V1 ensembles the quantized Efficientnet-Lite0 architecture for achieving high performance and low computation. To prove the excellence of the suggested framework, the effectiveness of the model is compared with that of other existing models by deploying it on different hardware architectures. The suggested framework brings the bright insights of deploying the learning algorithms on the hardware so that portable devices will be in the hands of the deaf and dumb community to learn the uniqueness of the ancient Tamil language.

Keywords

EfficientNet-Lite0, Hardware deployment, Tamil, Tamil Sign Language Recognition System, Quantized model.

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