Scene text detection method based on the hierarchical model

• Published in: IET computer vision Vol. 9; no. 4; pp. 500 - 510
• Main Authors: Zhou, Gang, Liu, Yuehu, Xu, Liang, Jia, Zhenhong
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.08.2015; Wiley
• Subjects: candidate text connected components; Color; Colour; Consistency; hierarchical model; image classification; image colour analysis; Image detection; image segmentation; Languages; MSRA-TD500 dataset; nontext CCs; scene images; scene text detection method; Segments; State of the art; text colours; text detection; text fonts; text languages; text scales; text-based information extraction systems; Texts; text scales; text fonts; image classification; scene text detection method; text-based information extraction systems; text languages; MSRA-TD500 dataset; text colours; scene images; image segmentation; nontext CCs; hierarchical model; image colour analysis; candidate text connected components; text detection
• ISSN: 1751-9632; 1751-9640; 1751-9640
• DOI: 10.1049/iet-cvi.2014.0297

As an important step in text-based information extraction systems, scene text detection has become a popular subject of research in recent years. In this study, the authors present a novel approach to robustly detect texts which are variable in scales, colours, fonts, languages and orientations in scene images. To segment candidate text connected components (CCs) from images, both local contrast and colour consistency are considered in superpixel level. To filter out the non-text CCs, a hierarchical model is designed. This hierarchical model groups the CCs into three cascaded stages, and is equipped with a well-designed classifier in each stage. Experimental results on the public ICDAR 2005 dataset and the MSRA-TD500 dataset show that their approach obtains better performance than other state-of-the-art methods.