  Video text detection is considered as one of the most difficult tasks in
document analysis due to the following two challenges: 1) the difficulties
caused by video scenes, i.e., motion blur, illumination changes, and occlusion;
2) the properties of text including variants of fonts, languages, orientations,
and shapes. Most existing methods attempt to enhance the performance of video
text detection by cooperating with video text tracking, but treat these two
tasks separately. In this work, we propose an end-to-end video text detection
model with online tracking to address these two challenges. Specifically, in
the detection branch, we adopt ConvLSTM to capture spatial structure
information and motion memory. In the tracking branch, we convert the tracking
problem to text instance association, and an appearance-geometry descriptor
with memory mechanism is proposed to generate robust representation of text
instances. By integrating these two branches into one trainable framework, they
can promote each other and the computational cost is significantly reduced.
Experiments on existing video text benchmarks including ICDAR2013 Video,
Minetto and YVT demonstrate that the proposed method significantly outperforms
state-of-the-art methods. Our method improves F-score by about 2 on all
datasets and it can run realtime with 24.36 fps on TITAN Xp.
