2022/04/05 by Xiang Zhang, Zhang, Xiang, Yongwen Su +5 · 12 citations
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Handwritten Text Recognition Techniques #Image Processing and 3D Reconstruction #Vehicle License Plate Recognition #cs.CV
paper · pdf · doi:10.48550/arxiv.2204.01918
Accepted to CVPR 2022
arxiv created 2022/04/05 · openalex publication_date 2022/04/05 · arxiv updated 2022/04/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.