Zero-shot Generation of Training Data with Denoising Diffusion
  Probabilistic Model for Handwritten Chinese Character Recognition
Dongnan Gui Kai Chen Haisong Ding Qiang Huo
There are more than 80,000 character categories in Chinese while most of them
are rarely used. To build a high performance handwritten Chinese character
recognition (HCCR) system supporting the full character set with a traditional
approach, many training samples need be collected for each character category,
which is both time-consuming and expensive. In this paper, we propose a novel
approach to transforming Chinese character glyph images generated from font
libraries to handwritten ones with a denoising diffusion probabilistic model
(DDPM). Training from handwritten samples of a small character set, the DDPM is
capable of mapping printed strokes to handwritten ones, which makes it possible
to generate photo-realistic and diverse style handwritten samples of unseen
character categories. Combining DDPM-synthesized samples of unseen categories
with real samples of other categories, we can build an HCCR system to support
the full character set. Experimental results on CASIA-HWDB dataset with 3,755
character categories show that the HCCR systems trained with synthetic samples
perform similarly with the one trained with real samples in terms of
recognition accuracy. The proposed method has the potential to address HCCR
with a larger vocabulary.