Sign Language Is A Crucial Means Of Communication For The Deaf And Hard Of Hearing Community. This Project Presents A Comprehensive Approach To Real-time Sign Language Detection Using Convolutional Neural Networks (CNN) And The You Only Look Once (YOLO) Object Detection Framework. The Primary Objective Of The Project Is To Bridge The Communication Gap Between Individuals Who Use Sign Language And Those Who May Not Understand It. Firstly, The Implementation Involves Real-time Voice Recognition To Detect Spoken Words And Translate Them Into Corresponding Alphabet Letter Sign Language Gestures. A CNN Model Is Trained On A Custom Dataset Of Sign Language Gestures Associated With Various Spoken Words. This Model Achieves Accurate Word Recognition, Enabling The Translation Of Spoken Language Into Visual Sign Representations. Secondly, The Project Integrates Live Camera Input To Detect Sign Language Gestures Directly From Hand Movements. The YOLO Framework Is Employed To Identify And Localize Individual Signs Corresponding To Letters Of The Alphabet. By Training The YOLO Model On An Annotated Dataset Of Hand Signs, The System Can Accurately Recognize And Display The Appropriate Letter For Each Detected Gesture. The Experimental Results Demonstrate The Effectiveness Of The Proposed Approach. The CNN Model Achieves High Accuracy In Recognizing Spoken Words, Facilitating Accurate Translation Into Sign Language. Additionally, The YOLO-based Sign Language Detection Exhibits Robustness In Identifying Different Sign Gestures From Live Camera Feed, With Real-time Performance Suitable For Practical Applications. The Implementation Of The System Is By Using OpenCV-Python. The System Uses Various Libraries.
