ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network
arXiv:2002.10200
Abstract
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 https://tinyurl.com/AdelaiDet
arXiv commentAccepted to Proc. IEEE Conf. Comp. Vis. Pattern Recogn. (CVPR) 2020
References in corpus (7)
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation
- TextBoxes: A Fast Text Detector with a Single Deep Neural Network
- TextField: Learning A Deep Direction Field for Irregular Scene Text Detection
- COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
- DeepText: A Unified Framework for Text Proposal Generation and Text Detection in Natural Images
- Towards End-to-End Text Spotting in Natural Scenes