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

Chinese character recognition has attracted much research interest due to its
wide applications. Although it has been studied for many years, some issues in
this field have not been completely resolved yet, e.g. the zero-shot problem.
Previous character-based and radical-based methods have not fundamentally
addressed the zero-shot problem since some characters or radicals in test sets
may not appear in training sets under a data-hungry condition. Inspired by the
fact that humans can generalize to know how to write characters unseen before
if they have learned stroke orders of some characters, we propose a
stroke-based method by decomposing each character into a sequence of strokes,
which are the most basic units of Chinese characters. However, we observe that
there is a one-to-many relationship between stroke sequences and Chinese
characters. To tackle this challenge, we employ a matching-based strategy to
transform the predicted stroke sequence to a specific character. We evaluate
the proposed method on handwritten characters, printed artistic characters, and
scene characters. The experimental results validate that the proposed method
outperforms existing methods on both character zero-shot and radical zero-shot
tasks. Moreover, the proposed method can be easily generalized to other
languages whose characters can be decomposed into strokes.

本文提出一种基于笔画的方法对中文字符进行识别，在解决中文字符零样本问题方面具有重要意义。在实验验证中，该方法比现有方法优异，不仅适用于手写文字，还适用于印刷艺术文字和场景文字。此外，该方法可以很容易地推广到可以分解成笔画的其他语言中的字符。

基于笔画分解的零样本中文字符识别

Zero-Shot Chinese Character Recognition with Stroke-Level Decomposition

Recently, great success has been achieved in offline handwritten Chinese
character recognition by using deep learning methods. Chinese characters are
mainly logographic and consist of basic radicals, however, previous research
mostly treated each Chinese character as a whole without explicitly considering
its internal two-dimensional structure and radicals. In this study, we propose
a novel radical analysis network with densely connected architecture (DenseRAN)
to analyze Chinese character radicals and its two-dimensional structures
simultaneously. DenseRAN first encodes input image to high-level visual
features by employing DenseNet as an encoder. Then a decoder based on recurrent
neural networks is employed, aiming at generating captions of Chinese
characters by detecting radicals and two-dimensional structures through
attention mechanism. The manner of treating a Chinese character as a
composition of two-dimensional structures and radicals can reduce the size of
vocabulary and enable DenseRAN to possess the capability of recognizing unseen
Chinese character classes, only if the corresponding radicals have been seen in
training set. Evaluated on ICDAR-2013 competition database, the proposed
approach significantly outperforms whole-character modeling approach with a
relative character error rate (CER) reduction of 18.54%. Meanwhile, for the
case of recognizing 3277 unseen Chinese characters in CASIA-HWDB1.2 database,
DenseRAN can achieve a character accuracy of about 41% while the traditional
whole-character method has no capability to handle them.

该研究提出了一种新的基于 DeepRAN 的方法，可以分析汉字的基本部首和二维结构，从而实现对未知字符的识别。