FOTS: Fast Oriented Text Spotting with a Unified Network
This addresses the challenge of real-time oriented text spotting in document analysis, offering a significant performance boost over previous methods.
The paper tackled the problem of incidental scene text spotting by proposing a unified end-to-end network that simultaneously performs text detection and recognition, achieving state-of-the-art results with over 5% improvement on ICDAR 2015 while running at 22.6 fps.
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.