  A novel framework named Markov Clustering Network (MCN) is proposed for fast
and robust scene text detection. MCN predicts instance-level bounding boxes by
firstly converting an image into a Stochastic Flow Graph (SFG) and then
performing Markov Clustering on this graph. Our method can detect text objects
with arbitrary size and orientation without prior knowledge of object size. The
stochastic flow graph encode objects' local correlation and semantic
information. An object is modeled as strongly connected nodes, which allows
flexible bottom-up detection for scale-varying and rotated objects. MCN
generates bounding boxes without using Non-Maximum Suppression, and it can be
fully parallelized on GPUs. The evaluation on public benchmarks shows that our
method outperforms the existing methods by a large margin in detecting
multioriented text objects. MCN achieves new state-of-art performance on
challenging MSRA-TD500 dataset with precision of 0.88, recall of 0.79 and
F-score of 0.83. Also, MCN achieves realtime inference with frame rate of 34
FPS, which is $1.5\times$ speedup when compared with the fastest scene text
detection algorithm.
