• Srihith Thummalapenta
• Sulaiman Shaik
• Abdul Suhail Shaik
• Mohan Krishna Vasili
• Dr.K.Santhi Sagar Reddy

This study explores the recognition of Indian Sign Language (ISL) using machine learning techniques. The research employs a dataset comprising ISL gestures and focuses on processing input images. Initially, the images are transformed into grayscale and binary formats, then subjected to median filtering for noise reduction and adjustment for enhanced clarity. Edge detection techniques are applied to highlight essential features. Feature extraction involves contour-based and region-based shape representation methods, along with the computation of Hu Moments features, capturing shape characteristics effectively. These features are combined into a unified vector representation. Support Vector Machine (SVM) classification is employed to classify gestures into predefined categories (A to Z). The accuracy of the classification process is evaluated to assess the effectiveness of the model in recognizing ISL gestures accurately. Through this approach, the study aims to develop a robust system for real-time recognition of sign language gestures, contributing to improved accessibility and communication for individuals using sign language.

Indian Sign language (ISL), Pre-Processing, Feature Extraction, support vector machine, machine learning, Classification, Accuracy.

" SIGN LANGUAGE RECOGNITION USING MACHINE LEARNING ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.240-247, June-2024, Available :http://www.jetir.org/papers/JETIRGJ06038.pdf

" SIGN LANGUAGE RECOGNITION USING MACHINE LEARNING ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp240-247, June-2024, Available at : http://www.jetir.org/papers/JETIRGJ06038.pdf