Wang Fangfang, Xu Xiaogang, Chen Yifeng, Li Xi
IEEE Trans Image Process. 2023;32:1-12. doi: 10.1109/TIP.2022.3201467. Epub 2022 Dec 19.
To robustly detect arbitrary-shaped scene texts, bottom-up methods are widely explored for their flexibility. Due to the highly homogeneous texture and cluttered distribution of scene texts, it is nontrivial for segmentation-based methods to discover the separatrixes between adjacent instances. To effectively separate nearby texts, many methods adopt the seed expansion strategy that segments shrunken text regions as seed areas, and then iteratively expands the seed areas into intact text regions. In seek of a more straightforward way that does not rely on seed area segmentation and avoid possible error accumulation brought by iterative processing, we propose a redundancy removal strategy. In this work, we directly explore two types of fuzzy semantics-text and separatrix-that do not possess specific boundaries, and separate cluttered instances by excluding the separatrix pixels from text regions. To deal with the fuzzy semantic boundaries, we also conduct reliability analysis in both optimization and inference stage to suppress false positive pixels at ambiguous locations. Experiments on benchmark datasets demonstrate the effectiveness of our method.
为了稳健地检测任意形状的场景文本，自底向上的方法因其灵活性而被广泛探索。由于场景文本具有高度均匀的纹理和杂乱的分布，基于分割的方法要发现相邻实例之间的分隔线并非易事。为了有效分离附近的文本，许多方法采用种子扩展策略，即将缩小的文本区域分割为种子区域，然后将种子区域迭代扩展为完整的文本区域。为了寻找一种更直接的方法，该方法不依赖种子区域分割且避免迭代处理带来的可能误差积累，我们提出了一种冗余去除策略。在这项工作中，我们直接探索两种不具有特定边界的模糊语义——文本和分隔线，并通过从文本区域中排除分隔线像素来分离杂乱的实例。为了处理模糊的语义边界，我们还在优化和推理阶段进行可靠性分析，以抑制模糊位置的误报像素。在基准数据集上的实验证明了我们方法的有效性。
