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This paper proposes a novel Convolutional Neural Network model for contourdata analysis (ContourCNN) and shape classification. A contour is a circularsequence of points representing a closed shape. For handling the cyclicalproperty of the contour representation, we employ circular convolution layers.Contours are often represented sparsely. To address information sparsity, weintroduce priority pooling layers that select features based on theirmagnitudes. Priority pooling layers pool features with low magnitudes whileleaving the rest unchanged. We evaluated the proposed model using letters anddigits shapes extracted from the EMNIST dataset and obtained a highclassification accuracy.

ContourCNN: convolutional neural network for contour data classification