character recognition; convolution; distortion; neural nets; CNN; PCCR; convolutional neural network; data augmentation; distorted sample generation technique; final convolutional layer; font styles; image-based Chinese character stroke local density; multifont printed Chinese character recognition; multipooling convolutional neural network; nonlinear transformation; nonlinear warping function; printed Chinese character recognition; single Chinese character grey-level distribution; single Chinese character layout; single Chinese character shape; spatial layout variations; Artificial neural networks; Handwriting recognition; Image recognition; Robustness; Data augmentation; Multi-font; Printed Chinese character recognition; convolutional neural network;
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