  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.
