I have a collection of physical chess books, many of which I also own in digital formats, either Kindle or PDF. They contain annotated games, diagrams, variations, and valuable analysis, but everything is locked inside static pages.
I wanted to turn these books into interactive ChessBase databases without manually entering hundreds of games and positions.
The solution combines Python, OCR, a Large Language Model (LLM), Stockfish, and my old ChessBase 15.
A Python program reads the PDF page by page. OCR extracts the text, while the LLM identifies games, moves, variations, and comments. Chess diagrams are converted into FEN positions, and games are reconstructed in PGN format, preserving the original annotations.
Stockfish checks the legality of moves and helps detect errors in the reconstructed positions.
Once the material is processed, a Python bot controls ChessBase 15, imports the PGN files, and organizes everything into a CBH database.
The result is a chess book transformed into an interactive study tool.
Instead of looking at static diagrams, I can replay games, explore variations, analyze positions with modern engines, and compare the author's ideas with current evaluations.
The interesting part is that none of these technologies needs to be reinvented. Python connects the components, AI interprets the content, Stockfish validates the chess positions, and ChessBase provides the study environment.
A chess book written fifty years ago can now be explored using today's technology.
This is a practical example of how AI can give new life to existing knowledge and software.