KText: Arbitrary shape text detection using modified K‐Means
Open Access
- 8 July 2021
- journal article
- research article
- Published by Institution of Engineering and Technology (IET) in IET Computer Vision
- Vol. 16 (1), 38-49
- https://doi.org/10.1049/cvi2.12052
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.Keywords
This publication has 34 references indexed in Scilit:
- SSD: Single Shot MultiBox DetectorPublished by Springer Science and Business Media LLC ,2016
- Detecting Text in Natural Image with Connectionist Text Proposal NetworkPublished by Springer Science and Business Media LLC ,2016
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksIEEE Transactions on Pattern Analysis and Machine Intelligence, 2016
- Synthetic Data for Text Localisation in Natural ImagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Fast R-CNNPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- ICDAR 2015 competition on Robust ReadingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Multi-Orientation Scene Text Detection with Adaptive ClusteringIEEE Transactions on Pattern Analysis and Machine Intelligence, 2015
- ICDAR 2013 Robust Reading CompetitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2013
- Handwritten Chinese text line segmentation by clustering with distance metric learningPattern Recognition, 2009
- Algorithm AS 136: A K-Means Clustering AlgorithmJournal of the Royal Statistical Society Series C: Applied Statistics, 1979