![]() Secondly, we utilize the extracted skeleton and the original image to train a GAN model, RNet (a stroke rendering network), to learn how to render the skeleton with stroke details in target style. Specifically, we first propose a fast skeleton extraction method (ENet). Therefore, in this work, we propose a three-stage Generative Adversarial Network (GAN) architecture for multi-chirography image translation, which is divided into skeleton extraction, skeleton transformation and stroke rendering with unpaired training data. In addition, unsupervised methods often cause the blurred and incorrect strokes. ![]() The supervised GAN-based methods require numerous image pairs, which is difficult for many chirography styles. ![]() Recently some GAN-based methods are proposed for font generation. Early methods on CH-Char synthesis are inefficient and require manual intervention. Different from English or general artistic style transfer, Chinese characters contain a large number of glyphs with the complicated content and characteristic style. ![]() The automatic style translation of Chinese characters (CH-Char) is a challenging problem. ![]()
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