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.

本研究提出了一种用于场景文本检测的新算法，其中提出了一组策略来显着提高文本定位的质量。具体而言，提出了一种文本特征对齐模块（TFAM），用于根据初始原始检测动态调整特征的感受域；设计了一种位置感知非极大值抑制模块（PA-NMS）来选择性地集中于可靠的原始检测，排除不可靠的检测，此外还提出了一种实例 IoU 损失来平衡针对不同比例的文本实例的训练。通过广泛的消融研究，证明了所提出策略的有效性和优越性。最终，将这些策略与业内领先的场景文本检测器 EAST 集成，实现了在保持快速运行速度的同时，达到了各种标准文本检测基准上的最新或有竞争力的性能。