KText: Arbitrary shape text detection using modified K‐Means

Abstract

Text detection methods based on grouping characters have emerged and have achieved promising performance. Nevertheless, previous methods that grouped characters by learning the relation of adjacent characters or used the heuristic clustering method with a handcrafted feature are unsuitable for dense, curved, or long texts. An effective manner of grouping characters is proposed by introducing K-Means that is modified by the law of universal gravitation, an outlier detection mechanism and sufficient context information. Based on that, corresponding text detector is presented, named the Text Detector, based on modified K-Means (KText), which can generate the bounding boundary of word-level texts with an arbitrary shape. In the experimental stage, two novel stratagems are presented to replenish character-level annotations to several datasets that provide only word-level annotations. To evaluate the effectiveness of the method, experiments are carried out on three benchmarks, ICDAR2013, ICDAR2015 and Total-Text, which contain horizontal, oriented and curved text. The results show that KText performs more competently than most state-of-the-art text detectors when handling dense texts with an arbitrary shape.