  Over the past few years, the field of scene text detection has progressed
rapidly that modern text detectors are able to hunt text in various challenging
scenarios. However, they might still fall short when handling text instances of
extreme aspect ratios and varying scales. To tackle such difficulties, we
propose in this paper a new algorithm for scene text detection, which puts
forward a set of strategies to significantly improve the quality of text
localization. Specifically, a Text Feature Alignment Module (TFAM) is proposed
to dynamically adjust the receptive fields of features based on initial raw
detections; a Position-Aware Non-Maximum Suppression (PA-NMS) module is devised
to selectively concentrate on reliable raw detections and exclude unreliable
ones; besides, we propose an Instance-wise IoU loss for balanced training to
deal with text instances of different scales. An extensive ablation study
demonstrates the effectiveness and superiority of the proposed strategies. The
resulting text detection system, which integrates the proposed strategies with
a leading scene text detector EAST, achieves state-of-the-art or competitive
performance on various standard benchmarks for text detection while keeping a
fast running speed.
