Rotation-Invariant Features for Multi-Oriented Text Detection in Natural Images
Identifieur interne : 000156 ( Main/Exploration ); précédent : 000155; suivant : 000157Rotation-Invariant Features for Multi-Oriented Text Detection in Natural Images
Auteurs : Cong Yao [République populaire de Chine] ; Xin Zhang [République populaire de Chine] ; Xiang Bai [République populaire de Chine] ; Wenyu Liu [République populaire de Chine] ; Yi Ma [République populaire de Chine] ; Zhuowen Tu [République populaire de Chine, États-Unis]Source :
- PLoS ONE [ 1932-6203 ] ; 2013.
Abstract
Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes.
Url:
DOI: 10.1371/journal.pone.0070173
PubMed: 23940544
PubMed Central: 3734103
Affiliations:
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