Sign Language Detection is a machine learning project that utilizes Support Vector Machine (SVM) and Random Forest classifiers to recognize American Sign Language (ASL) alphabet gestures. The system is designed to interpret hand signs in real time using a webcam or through uploaded images, converting these gestures into corresponding letters of the alphabet.

The application is intended to bridge communication between individuals who use ASL and those who may not be familiar with it. With support for the full ASL alphabet (A-Z) along and numbers and The most famous greeting words, this tool provides an accessible platform for interaction and education.

Key technologies include:

SVM: A supervised learning algorithm used for gesture classification. OpenCV: For image processing and webcam integration. Flask: To create an easy-to-use web interface. This project is a practical implementation of machine learning models to enhance communication through sign language recognition, providing an educational and assistive platform.