Scene Text Detection By Component Grouping

State-of-the-art methods have achieved impressive performances on multi-oriented text detection.Yet,they usually have difficulty in handling curved and dense texts,which are common in commodity images.In this thesis,we propose a network for detecting dense and arbitrary-shaped scene text by instance-aware component grouping(ICG),which is a flexible bottom-up method.To address the difficulty in separating dense text instances faced by most bottom-up methods,we propose attractive and repulsive link between text components which forces the network learning to focus more on close text instances,and instance-aware loss that fully exploits context to supervise the network.The final text detection is achieved by a modified minimum spanning tree(MST)algorithm based on the learned attractive and repulsive links.To demonstrate the effectiveness of the proposed method,we introduce a dense and arbitrary-shaped scene text dataset composed of commodity images(DAST1500).Experimental results show that the proposed ICG significantly outperforms state-of-the-art methods on DAST1500 and large-scale commodity image dataset MTWI.As for public benchmarks,ICG still surpasses other methods on two curved text datasets: Total-Text and CTW1500,and also achieves very competitive performance on multi-oriented dataset: ICDAR15(at 7.1FPS for 1280×768 image).