Text segmentation is a prerequisite in many real-world text-related tasks, e.g., text style transfer, and scene text removal. However, facing the lack of high-quality datasets and dedicated investigations, this critical prerequisite has been left as an assumption in many works, and has been largely overlooked by current research. To bridge this gap, we proposed TextSeg, a large-scale fine-annotated text dataset with six types of annotations: word- and character-wise bounding polygons, masks and transcriptions. We also introduce Text Refinement Network (TexRNet), a novel text segmentation approach that adapts to the unique properties of text, e.g. non-convex boundary, diverse texture, etc., which often impose burdens on traditional segmentation models. In our TexRNet, we propose text specific network designs to address such challenges, including key features pooling and attention-based similarity checking. We also introduce trimap and discriminator losses that show significant improvement on text segmentation. Extensive experiments are carried out on both our TextSeg dataset and other existing datasets. We demonstrate that TexRNet consistently improves text segmentation performance by nearly 2% compared to other state-of-the-art segmentation methods. Our dataset and code will be made available at https://github.com/SHI-Labs/Rethinking-Text-Segmentation.

本文提出了一个新的文本分割方法TexRNet和一个大规模的细致注释的文本数据集 TextSeg，通过引入文本特定的网络设计，例如关键特征池化和基于注意力的相似性检查，以及 trimap 和判别器损失来解决传统分割模型上存在的问题，包括非凸边界和各种纹理等，相比其他先进的分割方法，显示出2％以上的性能提升。

重构文本分割：一个新的数据集和文本特定的优化方法