Animating Chess Moves Recorded on the Chess Informant

In this study, we focus on animating chess games
recorded on chess informant. This involves recognition
of chess characters as well as moves and playing them
on chessboard. The proposed technique eliminates
false recognitions by means of controlling possible
moves in accordance with the rules of chess
(semantics). The paper produces solution for figurine
algebraic notation (FAN). For character and figure
recognition, we form feature vector including area,
center of area, perimeter, thinness ratio, aspect ratio,
compactness, Euler number, and projection. In the
recognition stage, multi-layer feed-forward (MLF)
neural network with back-propagation learning
algorithm is utilized to recognize characters and
figures of chess by using this feature vector. The
results show that the proposed system is capable of
contributing to the generation of robust game
databases through digitizing of chess games recorded
on chess informant.


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