Towards Accurate Instance-Level Text Spotting with Guided Attention

We tackle the text detection problem from the instance-aware segmentation perspective, in which text bounding boxes are directly extracted from segmentation results without location regression. Specifically, a text-specific attention model and a global enhancement block are introduced to enrich the semantics of text detection features. The attention model is trained with a weakly segmentation supervision signal and enforces the detector to focus on the text regions, while also suppressing the influence of neighboring background clutters. In conjunction with the attention model, a global enhancement block (GEB) is adapted to reason the relationship among different channels with channel-wise weights calibration. Our method achieves comparable performance with the recent state-of-the-arts on ICDAR2013, ICDAR2015, and ICDAR2017-MLT benchmark datasets.