Incidental scene text spotting is considered one of the most difficult and valuable challenges in the document analysis community. Most existing methods treat text detection and recognition as separate tasks. In this work, we propose a unified end-to-end trainable Fast Oriented Text Spotting (FOTS) network for simultaneous detection and recognition, sharing computation and visual information among the two complementary tasks. Specially, RoIRotate is introduced to share convolutional features between detection and recognition. Benefiting from convolution sharing strategy, our FOTS has little computation overhead compared to baseline text detection network, and the joint training method learns more generic features to make our method perform better than these two-stage methods. Experiments on ICDAR 2015, ICDAR 2017 MLT, and ICDAR 2013 datasets demonstrate that the proposed method outperforms state-of-the-art methods significantly, which further allows us to develop the first real-time oriented text spotting system which surpasses all previous state-of-the-art results by more than 5% on ICDAR 2015 text spotting task while keeping 22.6 fps.

此篇论文介绍了一种基于 Fast Oriented Text Spotting (FOTS) 网络的端到端可训练的文本检测和识别方法。使用 RoIRotate 的共享计算和视觉信息策略获得了比两阶段方法更优越的表现，并且提出的方法在 ICDAR 2015、ICDAR 2017 MLT 和 ICDAR 2013 数据集上的实验结果表明其性能显著优于先前的最先进方法。

FOTS: 基于统一网络的快速文本定位