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.

本文提出一种基于 DDPM 模型从字体库生成手写汉字样本的方法，通过将新类别的 DDPM 合成样本与其他类别的真实样本相结合，提出了一种支持完整字符集的 HCCR 系统，实验结果表明，合成的样本在识别准确率上与真实样本具有相似的性能。

使用去噪扩散概率模型进行零样本生成训练数据以提高手写汉字识别的准确性

Zero-shot Generation of Training Data with Denoising Diffusion  Probabilistic Model for Handwritten Chinese Character Recognition

In this work, we propose a technique that utilizes a fully convolutional
network (FCN) to localize image splicing attacks. We first evaluated a
single-task FCN (SFCN) trained only on the surface label. Although the SFCN is
shown to provide superior performance over existing methods, it still provides
a coarse localization output in certain cases. Therefore, we propose the use of
a multi-task FCN (MFCN) that utilizes two output branches for multi-task
learning. One branch is used to learn the surface label, while the other branch
is used to learn the edge or boundary of the spliced region. We trained the
networks using the CASIA v2.0 dataset, and tested the trained models on the
CASIA v1.0, Columbia Uncompressed, Carvalho, and the DARPA/NIST Nimble
Challenge 2016 SCI datasets. Experiments show that the SFCN and MFCN outperform
existing splicing localization algorithms, and that the MFCN can achieve finer
localization than the SFCN.

本文提出一种利用全卷积网络 (FCN) 实现图像拼接攻击定位的技术。采用单任务 FCN (SFCN) 并利用表面标签进行训练，但在某些情况下输出的定位结果粗糙。因此，作者提出采用多任务 FCN (MFCN)，其中利用两个输出分支进行多任务学习。实验结果表明，SFCN 和 MFCN 学习到的模型都能优于现有拼接定位算法，并且 MFCN 比 SFCN 具有更高的定位精度。