Please use this identifier to cite or link to this item:
https://doi.org/10.1145/2393347.2396307
DC Field | Value | |
---|---|---|
dc.title | Detecting text in the real world | |
dc.contributor.author | Phan, T.Q. | |
dc.contributor.author | Shivakumara, P. | |
dc.contributor.author | Tan, C.L. | |
dc.date.accessioned | 2013-07-04T08:29:25Z | |
dc.date.available | 2013-07-04T08:29:25Z | |
dc.date.issued | 2012 | |
dc.identifier.citation | 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. <a href="https://doi.org/10.1145/2393347.2396307" target="_blank">https://doi.org/10.1145/2393347.2396307</a> | |
dc.identifier.isbn | 9781450310895 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/41518 | |
dc.description.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. | |
dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1145/2393347.2396307 | |
dc.source | Scopus | |
dc.subject | gradient vector flow | |
dc.subject | natural scene text | |
dc.subject | scene text detection | |
dc.subject | street view images | |
dc.subject | symmetry detection | |
dc.subject | texture analysis | |
dc.type | Conference Paper | |
dc.contributor.department | COMPUTER SCIENCE | |
dc.description.doi | 10.1145/2393347.2396307 | |
dc.description.sourcetitle | MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia | |
dc.description.page | 765-768 | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Staff Publications |
Show simple item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.