Uni-Stroke Gesture Recognition from User Input

This project explores the practical application of the $1 Uni-Stroke Gesture Recognizer, a streamlined algorithm designed for efficient gesture recognition in user interfaces. Developed using Python and the Tkinter library, this recognizer achieves up to 98% accuracy in real-time gesture identification.

Project Overview:

• Efficient Gesture Recognition: Utilizes the $1 Uni-Stroke Gesture Recognizer algorithm, optimized to identify 16 unique gestures with minimal training data.
• Interactive User Interface: Features a user-friendly interface developed with Tkinter, enabling users to interact directly by performing gestures that are recognized and displayed on-screen.
• Enhanced Recognition Capabilities: By increasing the number of gesture templates from one to ten, the algorithm’s accuracy was significantly boosted, demonstrating improved performance with diverse input.
• User Data Collection: Designed a tool to collect gesture data in three distinct modes, which allows for comprehensive performance testing and user interaction analysis.

This project not only showcases advancements in gesture recognition technology but also serves as a versatile tool for developers and researchers interested in human-computer interaction and user interface design.