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。