• HARSHITHA
• RUCHI
• SAURAV KUMAR
• TEJAS V KANGOD
• SAMEER KUMAR SINGH

The "Real-Time Hand Gesture Detection for Sign Language Recognition using Python" project aims to develop a system for recognizing sign language gestures in realtime. It utilizes computer vision techniques and machine learning algorithms to detect and classify hand movements, achieving a training accuracy of 99.34% and validation accuracy of 99.00% with the Xception architecture model. The system, implemented in Python with OpenCV, detects gestures like "Ok," "Open Hand," "Peace," "Thumb," and "No Hand Detected." With a user-friendly interface, it facilitates non-sign language users, enhancing inclusivity in classrooms, workplaces, and public spaces

Sign Language Recognition, SLR, Real-time Hand Gesture Detection, Computer Vision, Machine Learning, Python, Deep Learning, Xception architecture, OpenCV, Neural Networks, Data Augmentation, Inception v3, LSTM, Inclusivity, Accessibility, User-Friendly Interface, GUI, Real-time Communication, Classroom Assistance, Workplace Assistance, Dataset Generation, Real-time Processing, ASL, ISL, Background Noise Filtering, Object Filtering, Human-Computer Interaction, HCI, Assistive Technology, Gesture Recognition, Pattern Recognition, Image Processing

"Sign Language Recognition: A Case Study", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.f613-f617, May-2024, Available :http://www.jetir.org/papers/JETIR2405571.pdf

"Sign Language Recognition: A Case Study", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppf613-f617, May-2024, Available at : http://www.jetir.org/papers/JETIR2405571.pdf