论文标题
文本发现变压器
Text Spotting Transformers
论文作者
论文摘要
在本文中，我们提出了文本发现变压器（TestR），这是一种通用的端到端文本斑点框架，使用变压器在野外进行文本检测和识别。 testR建立在单个编码器和双重解码器上，用于联合文本框控制点回归和字符识别。除大多数现有文献外，我们的方法还没有利益区域的操作和启发式驱动的后处理程序。在处理弯曲的文本框时，Testr特别有效，在这些文本框中需要特殊的护理来适应传统的边界框表示。我们显示了适用于Bezier曲线和多边形注释中的文本实例的控制点的规范表示。此外，我们设计了一个边界盒引导的多边形检测（框到聚合物）过程。在弯曲和任意形状的数据集上进行的实验证明了所提出的测试算法的最新性能。
In this paper, we present TExt Spotting TRansformers (TESTR), a generic end-to-end text spotting framework using Transformers for text detection and recognition in the wild. TESTR builds upon a single encoder and dual decoders for the joint text-box control point regression and character recognition. Other than most existing literature, our method is free from Region-of-Interest operations and heuristics-driven post-processing procedures; TESTR is particularly effective when dealing with curved text-boxes where special cares are needed for the adaptation of the traditional bounding-box representations. We show our canonical representation of control points suitable for text instances in both Bezier curve and polygon annotations. In addition, we design a bounding-box guided polygon detection (box-to-polygon) process. Experiments on curved and arbitrarily shaped datasets demonstrate state-of-the-art performances of the proposed TESTR algorithm.
