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Automatic digitization of chess games using computer vision is a significanttechnological challenge. This problem is of much interest for tournamentorganizers and amateur or professional players to broadcast theirover-the-board (OTB) games online or analyze them using chess engines. Previouswork has shown promising results, but the recognition accuracy and the latencyof state-of-the-art techniques still need further enhancements to allow theirpractical and affordable deployment. We have investigated how to implement themon an Nvidia Jetson Nano single-board computer effectively. Our firstcontribution has been accelerating the chessboard's detection algorithm.Subsequently, we have analyzed different Convolutional Neural Networks forchess piece classification and how to map them efficiently on our embeddedplatform. Notably, we have implemented a functional framework thatautomatically digitizes a chess position from an image in less than 1 second,with 92% accuracy when classifying the pieces and 95% when detecting the board.

LiveChess2FEN: a Framework for Classifying Chess Pieces based on CNNs