  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.
