Abstract A novel framework named Markov Clustering Network (MCN)is proposed for fast and robust scene text detec- tion.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 orien- tation without prior knowledge of object size.The stochas- tic flow graph encode objects'local correlation and se- mantic information.An object is modeled as strongly con- nected nodes,which allows flexible bottom-up detection for scale-varying and rotated objects.MCN generates bound- ing 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 detecring multioriented text objects.MCN achieves new state-of-art performance on challenging MSRA-TD500 dataset with precision of 0.88, recall of O.79 and F-score of 0.83.Also,MCN achieves re- altime inference with frame rate of 34 FPS,which is 1.5x speedup when compared with the fastest scene text detection algorithm.