Please use this identifier to cite or link to this item: https://doi.org/10.1145/2393347.2396307
Title: Detecting text in the real world
Authors: Phan, T.Q. 
Shivakumara, P. 
Tan, C.L. 
Keywords: gradient vector flow
natural scene text
scene text detection
street view images
symmetry detection
texture analysis
Issue Date: 2012
Source: Phan, T.Q.,Shivakumara, P.,Tan, C.L. (2012). Detecting text in the real world. MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia : 765-768. ScholarBank@NUS Repository. https://doi.org/10.1145/2393347.2396307
Abstract: The problem of text detection in natural scene images is challenging because of the unconstrained sizes, colors, backgrounds and alignments of the characters. This paper proposes novel symmetry features for this task. Within a text line, the intra-character symmetry captures the correspondence between the inner contour and the outer contour of a character while the inter-character symmetry helps to extract information from the gap region between two consecutive characters. A formulation based on Gradient Vector Flow is used to detect both types of symmetry points. These points are then grouped into text lines using the consistency in sizes, colors, and stroke and gap thickness. Therefore, unlike most existing methods which use only character features, our method exploits both the text features and the gap features to improve the detection result. Experimentally, our method compares well to the state-of-the-art on public datasets for natural scenes and street-level images, an emerging category of image data. The proposed technique can be used in a wide range of multimedia applications such as content-based image/video retrieval, mobile visual search and sign translation. © 2012 ACM.
Source Title: MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia
URI: http://scholarbank.nus.edu.sg/handle/10635/41518
ISBN: 9781450310895
DOI: 10.1145/2393347.2396307
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