Scene text detection and recognition have been well explored in the past few years. Despite the progress, efficient and accurate end-to-end spotting of arbitrarily-shaped text remains challenging. In this work, we propose an end-to-end text spotting framework, termed PAN++, which can efficiently detect and recognize text of arbitrary shapes in natural scenes. PAN++ is based on the kernel representation that reformulates a text line as a text kernel (central region) surrounded by peripheral pixels. By systematically comparing with existing scene text representations, we show that our kernel representation can not only describe arbitrarily-shaped text but also well distinguish adjacent text. Moreover, as a pixel-based representation, the kernel representation can be predicted by a single fully convolutional network, which is very friendly to real-time applications. Taking the advantages of the kernel representation, we design a series of components as follows: 1) a computationally efficient feature enhancement network composed of stacked Feature Pyramid Enhancement Modules (FPEMs); 2) a lightweight detection head cooperating with Pixel Aggregation (PA); and 3) an efficient attention-based recognition head with Masked RoI. Benefiting from the above designs, our method achieves high inference speed while maintaining competitive accuracy. Extensive experiments show the superiority of our method.

中文翻译：

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PAN ++：致力于高效，准确地端到端发现任意形状的文本。

在过去的几年中，场景文本的检测和识别已经得到了很好的探索。尽管取得了进步，但高效，准确地端到端发现任意形状的文本仍然具有挑战性。在这项工作中，我们提出了一种称为PAN ++的端到端文本发现框架，该框架可以有效地检测和识别自然场景中任意形状的文本。PAN ++基于内核表示形式，该表示形式将文本行重新构造为周围像素围绕的文本内核（中央区域）。通过与现有场景文本表示形式的系统比较，我们表明我们的内核表示形式不仅可以描述任意形状的文本，而且可以很好地区分相邻的文本。此外，作为基于像素的表示形式，内核表示形式可以通过单个完全卷积网络进行预测，这对实时应用程序非常友好。利用内核表示的优势，我们设计了以下一系列组件：1）一个由堆叠式特征金字塔增强模块（FPEM）组成的计算有效的特征增强网络；2）与像素聚合（PA）协作的轻量级检测头；3）具有Masked RoI的高效基于注意力的识别头。受益于上述设计，我们的方法在保持竞争准确性的同时实现了较高的推理速度。大量的实验证明了我们方法的优越性。2）与像素聚合（PA）协作的轻量级检测头；3）具有Masked RoI的高效基于注意力的识别头。受益于上述设计，我们的方法在保持竞争准确性的同时实现了较高的推理速度。大量的实验证明了我们方法的优越性。2）与像素聚合（PA）协作的轻量级检测头；3）具有Masked RoI的高效基于注意力的识别头。受益于上述设计，我们的方法在保持竞争准确性的同时实现了较高的推理速度。大量的实验证明了我们方法的优越性。