Saji, Alen CharuvilaAlen CharuvilaSajiRamalakshmi, KKRamalakshmiSenbagavalli, MMSenbagavalliGunasekaran, HemalathaHemalathaGunasekaranEbenezer, ShamilaShamilaEbenezer2025-03-212025-03-212022-01Vol. 8, No. 1; pp. 441-4462395-52952395-5287https://gnanaganga.alliance.edu.in/handle/123456789/4098The deaf-mute community utilises sign language for interacting among themselves and others. The introduction of standard sign language has made their lives much easier. This paper proposes an effective hand-sign recognition method using a deep learning technique and is based on YOLOv5, which is a real-time object detection algorithm which detects a hand sign and outputs the corresponding text. The proposed model utilises various sub-models namely, Cross Stage Partial Network (CSPNet), Path Aggregation Network (PANet), Dense Prediction. This model can be conveniently deployed into an android application with a user-friendly interface.YOLOv5ClassificationArrhythmiaDeep learningConvolution deep learningWebservicesArticle