Although current text detection algorithms demonstrate effectiveness in
general scenarios, their performance declines when confronted with
artistic-style text featuring complex structures. This paper proposes a method
that utilizes Criss-Cross Attention and residual dense block to address the
incomplete and misdiagnosis of artistic-style text detection by current
algorithms. Specifically, our method mainly consists of a feature extraction
backbone, a feature enhancement network, a multi-scale feature fusion module,
and a boundary discrimination module. The feature enhancement network
significantly enhances the model's perceptual capabilities in complex
environments by fusing horizontal and vertical contextual information, allowing
it to capture detailed features overlooked in artistic-style text. We
incorporate residual dense block into the Feature Pyramid Network to suppress
the effect of background noise during feature fusion. Aiming to omit the
complex post-processing, we explore a boundary discrimination module that
guides the correct generation of boundary proposals. Furthermore, given that
movie poster titles often use stylized art fonts, we collected a Movie-Poster
dataset to address the scarcity of artistic-style text data. Extensive
experiments demonstrate that our proposed method performs superiorly on the
Movie-Poster dataset and produces excellent results on multiple benchmark
datasets. The code and the Movie-Poster dataset will be available at:
this https URL

本文提出了一种利用交叉关注和残差密集块的方法，解决目前算法在具有复杂结构的艺术风格文本检测中存在的不完整和误诊问题。通过融合水平和垂直上下文信息来显著增强模型在复杂环境中的感知能力，并利用残差密集块来抑制背景噪声的影响，在不需要复杂的后处理的情况下引入边界鉴别模块来指导正确生成边界建议，实验证明所提出的方法在电影海报数据集上表现优异，并在多个基准数据集上获得出色的结果。