Linked Life Data

15376960

Source:http://linkedlifedata.com/resource/pubmed/id/15376960

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2004-9-20
pubmed:abstractText
In this paper, we present an approach to automatic detection and recognition of signs from natural scenes, and its application to a sign translation task. The proposed approach embeds multiresolution and multiscale edge detection, adaptive searching, color analysis, and affine rectification in a hierarchical framework for sign detection, with different emphases at each phase to handle the text in different sizes, orientations, color distributions and backgrounds. We use affine rectification to recover deformation of the text regions caused by an inappropriate camera view angle. The procedure can significantly improve text detection rate and optical character recognition (OCR) accuracy. Instead of using binary information for OCR, we extract features from an intensity image directly. We propose a local intensity normalization method to effectively handle lighting variations, followed by a Gabor transform to obtain local features, and finally a linear discriminant analysis (LDA) method for feature selection. We have applied the approach in developing a Chinese sign translation system, which can automatically detect and recognize Chinese signs as input from a camera, and translate the recognized text into English.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1057-7149
pubmed:author
pubmed:issnType
Print
pubmed:volume
13
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
87-99
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Automatic detection and recognition of signs from natural scenes.
pubmed:affiliation
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213 USA. xlchen@cs.cmu.edu
pubmed:publicationType
Journal Article, Comparative Study, Research Support, U.S. Gov't, Non-P.H.S., Evaluation Studies, Validation Studies