Abstract: Automating the conversion of chess literature into digital formats has become essential for enhancing training, analysis, and archiving. This work presents an intelligent system that automates the process of converting chess puzzles and games from PDF documents into PGN (Portable Game Notation) files. The approach begins with extracting high-resolution images from PDF pages, followed by image processing techniques such as adaptive thresholding and perspective transformation to detect and align chess boards. A Convolutional Neural Network (CNN) is then employed to recognize chess pieces on the board and generate accurate FEN (Forsyth-Edwards Notation) strings. These FEN strings are subsequently converted into PGN format, capturing not only board positions but also move sequences and annotations. The system significantly reduces the manual effort and has 98% accuracy, time required for transcribing chess games from books, offering a scalable and efficient solution for converting both classical and puzzle-based chess content into usable digital formats for modern chess databases and training applications..
Keywords: Chessboard recognition, PGN conversion, FEN, CNN, OCR, Tkinter GUI, image processing
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DOI:
10.17148/IJARCCE.2025.14549
