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Given an image, containing a 2D chessboard from the magazine "La settimana Enigmistica", recognize the chessboard and build the characters string according to the FEN encoding used as standard to describe the pieces distribution inside the chessboard

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Detect-Chessboard-FEN

Objective

Given an image, containing a 2D chessboard from the magazine "La settimana Enigmistica", recognize the chessboard e build the characters string according to the FEN encoding used as standard to describe the pieces distribution inside the chessboard

Objective

Approach of the algorithm

Approach

Chessboard segmentation: find the black square frame of the chessboard in the image

Projective transformation of the image: rectify the image and crop it around the checkerboard

Separation of the chessboard into 64 cells: find inside corners of the black frame and draw horizontal and verticals lines which separate the cells

Classification of the pieces contained in the cells: use a kNN classifier to recognize the cells(Bishop, Empty Square, King, etc..) and then find the pieces' colors and orientations

Find orientation of the chessboard: find the global orientation from the pieces orientation and eventually rotate the chessboard; if the chessboard gets rotated go back to step Separation of the chessboard into 64 cells, needed for the FEN string construction and for a better analysis of the cells' contents

Construction of the FEN string: build the FEN string according to the standard to describe the pieces distribution inside the chessboard

(This project is built to recognize the chessboards' images from a particular magazine, but the approach can be used for different styles of chessboards).

Constraints

  • Chessboard image should be taken from the magazine "La settimana Enigmistica"
  • The chessboard has to be the biggest square object with black contour
  • The checkerboard's black square frame must be completely visible
  • The image shouldn't be much blurred

Results

  • The kNN classifier from the confusion matrix has a precision of 100% with a Holdout for testing of 30%

  • Of the 64 images given, the algorithm can calculate the correct FEN string, based on the respective groundtruth file ".fen", of 57 images, so with a precision of 89%. (Consider the 062 and 063 images are knowingly given to be detected wrongly because of the constraints).

  • Regarding the orientation, the algorithm can correct the orientation of the chessboard with a precision of 97% (35 of 36 images)

Errors

(GT stands for groundtruth)

  • 004: segmentation error

  • 011: identification error of the color of a single piece

      GT: 2bb1K2/4p3/5k1n/3QNP2/5R1P/8/8/8 - 0 1 FEN: 2bb1K2/4p3/5k1n/3qNP2/5R1P/8/8/8 - 0 1
    
  • 020: classification error of a piece considered as an empty cell

      GT: 4rQB1/6n1/3p4/5b2/N2R4/1P6/2k5/KR6 - 0 1 FEN: 4rQB1/6p1/3p4/5b2/3R4/1P6/2k5/KR6 - 0 1
    
  • 032: orientation of the chessboard not identified correctly

  • 040: classification error of a piece considered as an empty cell

      GT: 1B6/5p2/2pnN1P1/R1N1k3/4p3/8/5Q2/7K - 0 1 FEN: 1B6/5p2/2pnN1P1/2N1k3/4p3/8/5Q2/7K - 0 1
    
  • 062: segmentation error (constraints not respected)

  • 063: segmentation error (constraints not respected)

Improvements to do

  • The algorithm is strong with dark, variations in brightness or in perspective images but weak with blurred images
  • Increase execution speed, now around 5 seconds per image
  • Make more precise the segmentation of the chessboard, also in cases in which the black frame is not perfectly intact due to image conditions
  • Find another method faster and more precise for identify the chessboard's orientation instead for template matching
  • Increase the dataset to reduce the number of pieces not classified correctly

More infos, descriptions and visual steps with images in the PDF file "Presentazione.pdf" (in italian)

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Given an image, containing a 2D chessboard from the magazine "La settimana Enigmistica", recognize the chessboard and build the characters string according to the FEN encoding used as standard to describe the pieces distribution inside the chessboard

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