In This Project, We Propose Using YOLOv7 To Recognize Sign Language Gestures. We Will Use A Dataset Of Images And Videos Of Individuals Performing Various Sign Language Gestures, And Train A YOLOv7 Model To Recognize These Gestures In Real-time. To Do This, We Will First Preprocess The Dataset By Extracting The Relevant Frames And Labeling The Sign Language Gestures. We Will Then Use YOLOv7 To Train A Neural Network To Recognize These Gestures. The Model Will Be Trained Using A Combination Of Image Augmentation Techniques And Transfer Learning To Improve Its Accuracy And Reduce Overfitting.
Once The Model Is Trained, We Will Use It To Detect And Classify Sign Language Gestures In Real-time Video Streams. We Will Use A Webcam To Capture Video Input And Apply The YOLOv7 Model To Recognize The Gestures. The Output Will Be Displayed On The Screen, Allowing Individuals Who Are Deaf Or Hard Of Hearing To Communicate More Easily.
