title: A unified framework for multioriented text detection and recognition.
abstract: High level semantics embodied in scene texts are both rich and clear and thus can serve as important cues for a wide range of vision applications, for instance, image understanding, image indexing, video search, geolocation, and automatic navigation. In this paper, we present a unified framework for text detection and recognition in natural images. The contributions of this paper are threefold: 1) text detection and recognition are accomplished concurrently using exactly the same features and classification scheme; 2) in contrast to methods in the literature, which mainly focus on horizontal or near-horizontal texts, the proposed system is capable of localizing and reading texts of varying orientations; and 3) a new dictionary search method is proposed, to correct the recognition errors usually caused by confusions among similar yet different characters. As an additional contribution, a novel image database with texts of different scales, colors, fonts, and orientations in diverse real-world scenarios, is generated and released. Extensive experiments on standard benchmarks as well as the proposed database demonstrate that the proposed system achieves highly competitive performance, especially on multioriented texts.
authors.forename: Cong
authors.initials: C
authors.lastname: Yao
authors.forename: Xiang
authors.initials: X
authors.lastname: Bai
authors.forename: Wenyu
authors.initials: W
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004282:Documentation / Q000145:classification*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods; D011932:Reading; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014956:Writing