Generating Handwritten Chinese Characters using CycleGAN

Handwriting of Chinese has long been an important skill in East Asia. However, automatic generation of handwritten Chinese characters poses a great challenge due to the large number of characters. Various machine learning techniques have been used to recognize Chinese characters, but few works have studied the handwritten Chinese character generation problem, especially with unpaired training data. In this work, we formulate the Chinese handwritten character generation as a problem that learns a mapping from an existing printed font to a personalized handwritten style. We further propose DenseNet CycleGAN to generate Chinese handwritten characters. Our method is applied not only to commonly used Chinese characters but also to calligraphy work with aesthetic values. Furthermore, we propose content accuracy and style discrepancy as the evaluation metrics to assess the quality of the handwritten characters generated. We then use our proposed metrics to evaluate the generated characters from CASIA dataset as well as our newly introduced Lanting calligraphy dataset.Comment: Accepted at WACV 201

Style Transfer and Extraction for the Handwritten Letters Using Deep Learning

How can we learn, transfer and extract handwriting styles using deep neural networks? This paper explores these questions using a deep conditioned autoencoder on the IRON-OFF handwriting data-set. We perform three experiments that systematically explore the quality of our style extraction procedure. First, We compare our model to handwriting benchmarks using multidimensional performance metrics. Second, we explore the quality of style transfer, i.e. how the model performs on new, unseen writers. In both experiments, we improve the metrics of state of the art methods by a large margin. Lastly, we analyze the latent space of our model, and we see that it separates consistently writing styles.Comment: Accepted in ICAART 201

Analysis of Writing Styles on Wood Slips of the Chinese Han Period Using Deep Generative Models

In this paper, we develop a method to analyze the calligraphic styles of wood slips excavated in Northwestern China. Specifically, we propose a method to quantify the degree of collapse of the Chinese characters by measuring the degree of dissociation using a deep generative model. To realize the method, we introduce anomaly detection with generative adversarial networks (AnoGAN), trained by normal data, and then detect abnormal data based on the reconstruction error when those data are input. First, we train the GAN by using images of a character written in Clerical-Script as training data. Next, we calculate the anomaly scores of the characters based on the difference between the Cursive-Script characters and the generated character images and then calculate the degree of collapse. In our experiments, we created datasets consisting of wood slips made in the Han Period and showed that the proposed method could quantify the degree of misalignment between Clerical-Script of neat font and Cursive-Script of scrawl font

数字书法研究综述

数字书法将传统书法的创作工具、视觉艺术效果、书写技巧和创作等用数字化的方式再现.本文首先在回顾数字书法研究历史和研究特点的基础上,给出数字书法的主要研究内容和研究方向,包括:书法工具的数字化建模、书法图像的分析与处理、书法字形的合成与美化等;然后阐述数字书法每一类问题的具体研究目标、研究现状和研究趋势;最后,探讨数字书法进一步发展需要予以关注的重要问题和研究方向.国家自然科学基金(批准号:61772440);;航空科学基金(批准号:20165168007)资助项