Authors : Dr. Girish Katkar; Shalaka Gaikwad; Dr. Ajay Ramteke

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/5dfppmkk

Scribd : https://tinyurl.com/4x6b7uk2

DOI : https://doi.org/10.38124/ijisrt/26apr960

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In this work, we propose an efficient hybrid framework for recognizing Indian Sign Language (ISL) alphabets by combining deep feature extraction with classical machine learning and optimization techniques. A pre- trained MobileNetV2 network is utilized to extract discriminative visual features from hand gesture images. These highdimensional features are subsequently compressed using Principal Component Analysis (PCA) to eliminate redundancy and improve computational efficiency. Particle Swarm Optimization (PSO) is then employed to determine optimal hyperparameters for a Support Vector Machine (SVM) classifier. The proposed system is evaluated on a 26-class ISL dataset and achieves an overall accuracy of 96.17%, along with consistently high precision, recall, and F1-scores. Further validation using confusion matrix and ROC analysis demonstrates the robustness and strong class separability of the model.

Keywords : Indian Sign Language Recognition, MobileNetV2, PCA, PSO, SVM.

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