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• W3016262012 abstract "This paper mainly discusses the generation of personalized fonts as the problem of image style transfer. The main purpose of this paper is to design a network framework that can extract and recombine the content and style of the characters. These attempts can be used to synthesize the entire set of fonts with only a small amount of characters. The paper combines various depth networks such as Convolutional Neural Network, Multi-layer Perceptron and Residual Network to find the optimal model to extract the features of the fonts character. The result shows that those characters we have generated is very close to real characters, using Structural Similarity index and Peak Signal-to-Noise Ratio evaluation criterions." @default.
• W3016262012 created "2020-04-17" @default.
• W3016262012 creator A5031783123 @default.
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• W3016262012 date "2020-03-28" @default.
• W3016262012 modified "2023-09-27" @default.
• W3016262012 title "Multiform Fonts-to-Fonts Translation via Style and Content Disentangled Representations of Chinese Character." @default.
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• W3016262012 hasPublicationYear "2020" @default.
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