  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.
