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sign language recognition using machine learning

Abstract:
The deaf and voiceless community uses sign language to interact with others, but the main difficulty they encounter is that not everyone can comprehend sign language. This system's primary goal is to close the communication gap between communities, which will enable the community of the voiceless to interact with others. The shape and position of hand motions vary from person to person, creating a linearity difficulty. Recent systems have developed a number of methods and techniques to solve the issue and construct this system. Previously, hand gesture movements were decoded using algorithms like KNearest Neighbors (KNN), Multi-class Super Vector Machine (SVM), and trials with hand gloves. The KNN, SVM, and CNN algorithms are compared in this study to see which one would offer the most accuracy overall. To achieve accuracy of 93.83%, 88.89%, and 98.49% for the KNN, SVM, and CNN models, approximately 29,000 pictures were divided into test and train data and preprocessed to fit. final year project is based on IEEE Paper.this will be one of the best Final year engineering project for computer science. Components that we will provide are.
1.complete documentation support
2.complete working hardware/software implemented in students environment
3.classes will held accordingly.

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