Electrical engineering

Frame (networking)

Heuristics

Pattern recognition (psychology)

Mathematics

Bounding overwatch

Operating system

Algorithm

Image (mathematics)

Encoder

Minimum bounding box

Engineering

Voltage

Artificial intelligence

Bézier curve

Polygon (computer graphics)

Geometry

Spotting

Computer science

Keyword spotting

Transformer

Telecommunications

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 tra-ditional bounding-box representations. We show our canonical representation of control points suitable for text in-stances 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.

Text Spotting Transformers