End-to-end text-spotting, which aims to integrate detection and recognition
in a unified framework, has attracted increasing attention due to its
simplicity of the two complimentary tasks. It remains an open problem
especially when processing arbitrarily-shaped text instances. Previous methods
can be roughly categorized into two groups: character-based and
segmentation-based, which often require character-level annotations and/or
complex post-processing due to the unstructured output. Here, we tackle
end-to-end text spotting by presenting Adaptive Bezier Curve Network v2 (ABCNet
v2). Our main contributions are four-fold: 1) For the first time, we adaptively
fit arbitrarily-shaped text by a parameterized Bezier curve, which, compared
with segmentation-based methods, can not only provide structured output but
also controllable representation. 2) We design a novel BezierAlign layer for
extracting accurate convolution features of a text instance of arbitrary
shapes, significantly improving the precision of recognition over previous
methods. 3) Different from previous methods, which often suffer from complex
post-processing and sensitive hyper-parameters, our ABCNet v2 maintains a
simple pipeline with the only post-processing non-maximum suppression (NMS). 4)
As the performance of text recognition closely depends on feature alignment,
ABCNet v2 further adopts a simple yet effective coordinate convolution to
encode the position of the convolutional filters, which leads to a considerable
improvement with negligible computation overhead. Comprehensive experiments
conducted on various bilingual (English and Chinese) benchmark datasets
demonstrate that ABCNet v2 can achieve state-of-the-art performance while
maintaining very high efficiency.

本论文介绍了一种适应性贝塞尔曲线网络 v2 (ABCNet v2) 的方法，该方法通过贝塞尔曲线适应性地拟合任意形状的文本，设计新颖的 BezierAlign 层提高了识别精度，简化了流程，并通过简单但有效的坐标卷积进一步提高了性能，经过实验证明在各种双语基准数据集上实现了最先进的性能。

实时端到端文本检测的自适应贝塞尔曲线网络 - ABCNet v2

ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text  Spotting

Scene text detection and recognition has received increasing research
attention. Existing methods can be roughly categorized into two groups:
character-based and segmentation-based. These methods either are costly for
character annotation or need to maintain a complex pipeline, which is often not
suitable for real-time applications. Here we address the problem by proposing
the Adaptive Bezier-Curve Network (ABCNet). Our contributions are three-fold:
1) For the first time, we adaptively fit arbitrarily-shaped text by a
parameterized Bezier curve. 2) We design a novel BezierAlign layer for
extracting accurate convolution features of a text instance with arbitrary
shapes, significantly improving the precision compared with previous methods.
3) Compared with standard bounding box detection, our Bezier curve detection
introduces negligible computation overhead, resulting in superiority of our
method in both efficiency and accuracy. Experiments on arbitrarily-shaped
benchmark datasets, namely Total-Text and CTW1500, demonstrate that ABCNet
achieves state-of-the-art accuracy, meanwhile significantly improving the
speed. In particular, on Total-Text, our realtime version is over 10 times
faster than recent state-of-the-art methods with a competitive recognition
accuracy. Code is available at this https URL

本研究提出在文字检测和识别中应用自适应贝塞尔曲线网络 (ABCNet) 以适应任意形状文字，通过设计新颖的 BezierAlign 层提取准确的卷积特征。ABCNet 相较于现存方法具有更高的速度和准确性，适用于实时应用。