We used ESRGAN as the basic network structure and applied dilated convolution and a novel loss function that improve the quality of reconstructed characters. This paper proposes a method for separating Chinese characters based on generative adversarial network (GAN). ![]() The complex structure of Chinese characters makes it difficult to obtain the goal because of easy loss of fine details and overall structure in reconstructed characters. Separating printed or handwritten characters from a noisy background is valuable for many applications including test paper autoscoring.
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