Abstract
Connected component (CC) is a proper text shape representation that aligns with human reading intuition. However, CC-based text detection methods have recently faced a developmental bottleneck that their time-consuming post-processing is difficult to eliminate. To address this issue, we introduce an explicit relational reasoning network (ERRNet) to elegantly model the component relationships without post-processing. Concretely, we first represent each text instance as multiple ordered text components, and then treat these components as objects in sequential movement. In this way, scene text detection can be innovatively viewed as a tracking problem. From this perspective, we design an end-to-end tracking decoder to achieve a CC-based method dispensing with post-processing entirely. Additionally, we observe that there is an inconsistency between classification confidence and localization quality, so we propose a Polygon Monte-Carlo method to quickly and accurately evaluate the localization quality. Based on this, we introduce a position-supervised classification loss to guide the task-aligned learning of ERRNet. Experiments on challenging benchmarks demonstrate the effectiveness of our ERRNet. It consistently achieves state-of-the-art accuracy while holding highly competitive inference speed.
Abstract (translated)
连通组件（CC）是一种与人类阅读直觉相符的合适文本形状表示方法。然而，基于CC的文本检测方法最近遇到了一个发展瓶颈，即它们耗时较长的后处理步骤难以消除。为了解决这一问题，我们引入了一种显式关系推理网络（ERRNet），以优雅地建模组件间的关系而无需进行后处理。具体来说，我们将每个文本实例表示为多个有序的文本组件，并将这些组件视为一系列运动中的对象。通过这种方式，场景文本检测可以被创新性地视作一个跟踪问题。从这个角度来看，我们设计了一个端到端的跟踪解码器来实现一种完全不依赖后处理的基于CC的方法。此外，我们注意到分类置信度与定位质量之间存在不一致性，因此提出了一种多边形蒙特卡洛方法以快速准确地评估定位质量。基于此，我们引入了位置监督分类损失函数来指导ERRNet的任务对齐学习。在具有挑战性的基准测试上进行的实验表明我们的ERRNet是有效的，它不仅始终实现了最先进的准确性，而且保持了高度竞争力的推理速度。
URL
https://arxiv.org/abs/2412.14692
