  Chess game position analysis is important in improving ones game. It requires
entry of moves into a chess engine which is, cumbersome and error prone. We
present ARChessAnalyzer, a complete pipeline from live image capture of a
physical chess game, to board and piece recognition, to move analysis and
finally to Augmented Reality (AR) overlay of the chess diagram position and
move on the physical board. ARChessAnalyzer is like a scene analyzer - it uses
an ensemble of traditional image and vision techniques to segment the scene (ie
the chess game) and uses Convolution Neural Networks (CNNs) to predict the
segmented pieces and combine it together to analyze the game. This paper
advances the state of the art in the first of its kind end to end integration
of robust detection and segmentation of the board, chess piece detection using
the fine-tuned AlexNet CNN and chess engine analyzer in a handheld device app.
The accuracy of the entire chess position prediction pipeline is 93.45\% and
takes 3-4.5sec from live capture to AR overlay. We also validated our
hypothesis that ARChessAnalyzer, is faster at analysis than manual entry for
all board positions for valid outcomes. Our hope is that the instantaneous
feedback this app provides will help chess learners worldwide at all levels
improve their game.
