chess-recognition Computer Vision Project

Here are a few use cases for this project:

1. Online Chess Tutoring: The chess-recognition model can be used in online chess tutoring platforms, where the model identifies the chess pieces from an image of the physical board setup by the student or tutor, and then recreates the board digitally. This allows for better analysis, guidance, and real-time feedback as they play.

2. Automated Chess Game Analysis: Users can upload images of various board positions from a completed game or a series of critical moments in a game, and the model will identify the pieces and their locations. The system can then provide insights, tactical analysis, or suggest alternative moves for further learning and practice.

3. Chess Notation Conversion: The model can automatically convert images of chessboard setups into commonly used chess notations such as FEN (Forsyth-Edwards Notation) or PGN (Portable Game Notation). This helps players share, analyze, and store their chess games in a more compact and human-readable format.

4. Accessible Chess Interface for the Visually Impaired: By integrating the chess-recognition model with speech-to-text and text-to-speech technology, the system can verbally guide visually impaired players through a game or tournament, by describing the current board setup, piece movements, and possible next moves.

5. Designing Adaptive Chess Puzzles: The chess-recognition model can be incorporated into a puzzle generator, creating personalized puzzles based on real-life images of various chess positions or using existing master-level games as input. The system could then adjust the puzzle difficulty based on the skill level of the user, providing a customized learning experience for players of all levels.