Scene text detection techniques have garnered significant attention due to
their wide-ranging applications. However, existing methods have a high demand
for training data, and obtaining accurate human annotations is labor-intensive
and time-consuming. As a solution, researchers have widely adopted synthetic
text images as a complementary resource to real text images during
pre-training. Yet there is still room for synthetic datasets to enhance the
performance of scene text detectors. We contend that one main limitation of
existing generation methods is the insufficient integration of foreground text
with the background. To alleviate this problem, we present the Diffusion Model
based Text Generator (DiffText), a pipeline that utilizes the diffusion model
to seamlessly blend foreground text regions with the background's intrinsic
features. Additionally, we propose two strategies to generate visually coherent
text with fewer spelling errors. With fewer text instances, our produced text
images consistently surpass other synthetic data in aiding text detectors.
Extensive experiments on detecting horizontal, rotated, curved, and line-level
texts demonstrate the effectiveness of DiffText in producing realistic text
images.

通过扩充背景与前景文字的整合，Diffusion Model based Text Generator (DiffText) 创造了一种能生成真实文本图像并优化文本探测器性能的新型合成数据集方法。

使用扩散模型增强场景文本检测器的真实文本图像合成

Enhancing Scene Text Detectors with Realistic Text Image Synthesis Using  Diffusion Models

Prompt engineering is a powerful tool used to enhance the performance of
pre-trained models on downstream tasks. For example, providing the prompt
``Let's think step by step" improved GPT-3's reasoning accuracy to 63% on
MutiArith while prompting ``a photo of" filled with a class name enables CLIP
to achieve $80$\% zero-shot accuracy on ImageNet. While previous research has
explored prompt learning for the visual modality, analyzing what constitutes a
good visual prompt specifically for image recognition is limited. In addition,
existing visual prompt tuning methods' generalization ability is worse than
text-only prompting tuning. This paper explores our key insight: synthetic text
images are good visual prompts for vision-language models! To achieve that, we
propose our LoGoPrompt, which reformulates the classification objective to the
visual prompt selection and addresses the chicken-and-egg challenge of first
adding synthetic text images as class-wise visual prompts or predicting the
class first. Without any trainable visual prompt parameters, experimental
results on 16 datasets demonstrate that our method consistently outperforms
state-of-the-art methods in few-shot learning, base-to-new generalization, and
domain generalization.

本文通过引入合成文本图像作为类别相关的视觉提示，探索了视觉 - 语言模型中视觉提示的作用，并提出了一种 LoGoPrompt 方法，该方法在少样本学习、从基础到新领域的泛化以及领域泛化方面表现出一致优于现有方法的实验结果。