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Chess game position analysis is important in improving ones game. It requiresentry of moves into a chess engine which is, cumbersome and error prone. Wepresent ARChessAnalyzer, a complete pipeline from live image capture of aphysical chess game, to board and piece recognition, to move analysis andfinally to Augmented Reality (AR) overlay of the chess diagram position andmove on the physical board. ARChessAnalyzer is like a scene analyzer - it usesan ensemble of traditional image and vision techniques to segment the scene (iethe chess game) and uses Convolution Neural Networks (CNNs) to predict thesegmented pieces and combine it together to analyze the game. This paperadvances the state of the art in the first of its kind end to end integrationof robust detection and segmentation of the board, chess piece detection usingthe 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\% andtakes 3-4.5sec from live capture to AR overlay. We also validated ourhypothesis that ARChessAnalyzer, is faster at analysis than manual entry forall board positions for valid outcomes. Our hope is that the instantaneousfeedback this app provides will help chess learners worldwide at all levelsimprove their game.

Augmented Reality Chess Analyzer (ARChessAnalyzer): In-Device Inference  of Physical Chess Game Positions through Board Segmentation and Piece  Recognition using Convolutional Neural Network