All you need is a second look: Towards Tighter Arbitrary shape text detection

Deep learning-based scene text detection methods have progressed substantially over the past years. However, there remain several problems to be solved. Generally, long curve text instances tend to be fragmented because of the limited receptive field size of CNN. Besides, simple representations using rectangle or quadrangle bounding boxes fall short when dealing with more challenging arbitrary-shaped texts. In addition, the scale of text instances varies greatly which leads to the difficulty of accurate prediction through a single segmentation network. To address these problems, we innovatively propose a two-stage segmentation based arbitrary text detector named textitNASK (Need A Second looK). Specifically, textitNASK consists of a Text Instance Segmentation network namely textitTIS ((1st) stage), a Text RoI Pooling module and a Fiducial pOint eXpression module termed as textitFOX ((2nd) stage). Firstly, textitTIS conducts instance segmentation to obtain rectangle text proposals with a proposed Group Spatial and Channel Attention module (textitGSCA) to augment the feature expression. Then, Text RoI Pooling transforms these rectangles to the fixed size. Finally, textitFOX is introduced to reconstruct text instances with a more tighter representation using the predicted geometrical attributes including text center line, text line orientation, character scale and character orientation. Experimental results on two public benchmarks including textitTotal-Text and textitSCUT-CTW1500 have demonstrated that the proposed textitNASK achieves state-of-the-art results.