  Scene text detection is an important step of scene text recognition system
and also a challenging problem. Different from general object detection, the
main challenges of scene text detection lie on arbitrary orientations, small
sizes, and significantly variant aspect ratios of text in natural images. In
this paper, we present an end-to-end trainable fast scene text detector, named
TextBoxes++, which detects arbitrary-oriented scene text with both high
accuracy and efficiency in a single network forward pass. No post-processing
other than an efficient non-maximum suppression is involved. We have evaluated
the proposed TextBoxes++ on four public datasets. In all experiments,
TextBoxes++ outperforms competing methods in terms of text localization
accuracy and runtime. More specifically, TextBoxes++ achieves an f-measure of
0.817 at 11.6fps for 1024*1024 ICDAR 2015 Incidental text images, and an
f-measure of 0.5591 at 19.8fps for 768*768 COCO-Text images. Furthermore,
combined with a text recognizer, TextBoxes++ significantly outperforms the
state-of-the-art approaches for word spotting and end-to-end text recognition
tasks on popular benchmarks. Code is available at:
https://github.com/MhLiao/TextBoxes_plusplus
