A large number of annotated training images is crucial for training
successful scene text recognition models. However, collecting sufficient
datasets can be a labor-intensive and costly process, particularly for
low-resource languages. To address this challenge, auto-generating text data
has shown promise in alleviating the problem. Unfortunately, existing scene
text generation methods typically rely on a large amount of paired data, which
is difficult to obtain for low-resource languages. In this paper, we propose a
novel weakly supervised scene text generation method that leverages a few
recognition-level labels as weak supervision. The proposed method is able to
generate a large amount of scene text images with diverse backgrounds and font
styles through cross-language generation. Our method disentangles the content
and style features of scene text images, with the former representing textual
information and the latter representing characteristics such as font,
alignment, and background. To preserve the complete content structure of
generated images, we introduce an integrated attention module. Furthermore, to
bridge the style gap in the style of different languages, we incorporate a
pre-trained font classifier. We evaluate our method using state-of-the-art
scene text recognition models. Experiments demonstrate that our generated scene
text significantly improves the scene text recognition accuracy and help
achieve higher accuracy when complemented with other generative methods.

本文提出了一种基于弱监督学习的场景文本生成方法，通过跨语言生成，将场景文本图片的内容和样式特征分离，并引入整合式注意力模块和预先训练的字体分类器以保证生成图片的完整内容结构以及填补不同语言文本风格之间的差距，实验结果显示，该方法显著提高了场景文本识别精度。

低资源语言的弱监督场景文本生成

Weakly Supervised Scene Text Generation for Low-resource Languages

In this paper, we investigate the usage of fine-grained font recognition on
OCR for books printed from the 15th to the 18th century. We used a newly
created dataset for OCR of early printed books for which fonts are labeled with
bounding boxes. We know not only the font group used for each character, but
the locations of font changes as well. In books of this period, we frequently
find font group changes mid-line or even mid-word that indicate changes in
language. We consider 8 different font groups present in our corpus and
investigate 13 different subsets: the whole dataset and text lines with a
single font, multiple fonts, Roman fonts, Gothic fonts, and each of the
considered fonts, respectively. We show that OCR performance is strongly
impacted by font style and that selecting fine-tuned models with font group
recognition has a very positive impact on the results. Moreover, we developed a
system using local font group recognition in order to combine the output of
multiple font recognition models, and show that while slower, this approach
performs better not only on text lines composed of multiple fonts but on the
ones containing a single font only as well.

该论文研究了使用精细字体识别技术对 15 至 18 世纪印刷书籍进行 OCR 的方法。作者们使用了一个新的数据集，其中包含了早期印刷书籍的 OCR 信息，并标记了每个字体的边界框。研究结果表明，字体风格对 OCR 性能有很大影响，所选模型的精细程度对结果也有积极影响。此外，作者们开发了一个本地字体组识别系统，可以在识别多种字体的文本行时取得更好的识别效果。

结合 OCR 模型阅读早期印刷书籍

Combining OCR Models for Reading Early Modern Printed Books

Rubbing restorations are significant for preserving world cultural history.
In this paper, we propose the RubbingGAN model for restoring incomplete rubbing
characters. Specifically, we collect characters from the Zhang Menglong Bei and
build up the first rubbing restoration dataset. We design the first generative
adversarial network for rubbing restoration. Based on the dataset we collect,
we apply the RubbingGAN to learn the Zhang Menglong Bei font style and restore
the characters. The results of experiments show that RubbingGAN can repair both
slightly and severely incomplete rubbing characters fast and effectively.

本文介绍了 RubbingGAN 模型，通过建立第一个擦拓字符修复数据集和学习张梦龙碑体字样来修复擦拓字符，实验结果表明，RubbingGAN 可以快速有效地修复擦拓字符。