Generating Handwritten Chinese Characters using CycleGAN
This addresses the challenge of personalized handwriting generation for Chinese characters, which is incremental as it adapts an existing method to a specific domain.
The paper tackles the problem of generating handwritten Chinese characters from printed fonts using unpaired training data, proposing DenseNet CycleGAN to achieve this mapping and introducing new evaluation metrics for quality assessment.
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.