Automatic Chinese Font Generation System Reflecting Emotions Based on Generative Adversarial Network

Abstract

Manual font design is difficult and requires professional knowledge and skills to perform. Therefore, how to automatically generate the required fonts is a very challenging research task. On the other hand, there are few people who have studied the relationship between fonts and emotions, and common fonts generally cannot reflect emotional information. This paper proposes an Emotional Guidance GAN: an automatic Chinese font generation framework based on Generative Adversarial Network (GAN), which enables the generated fonts to reflect human emotional information. First, an elaborated questionnaire system was developed from Tencent company, which aims to quantitatively figure out the relationship between fonts and emotions. A visual expression recognition part is designed based on the trained model to provide a font generation module with conditional information. Moreover, the Emotional Guidance GAN (EG-GAN) with EM Distance and Gradient Penalty, as well as classification strategies, is proposed to generate new fonts with combined multiple styles that infer by an expression recognition module. The results of the evaluation experiments and the resolution of the synthesized font characters show the credibility of our model.

URL generative recognition method based on generative countermeasure network

“…In reference [1], a character convolutional neural network is designed to identify bad URLs containing illegal character content texts, and a deep learning method is used to mine the data features of historical illegal URLs and establish a rapid identification model of illegal URLs, which has a good identification effect. Literature [2] uses CATBL algorithm to extract the global and local features of URL, fuses these features, uses CNN to mine deep-seated local features, uses Attention mechanism to adjust the weight and two-way LSTM to extract global features, which are used to detect malicious URLs in the network. It also compares the similar features of URL files.…”