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  1. International Journal of Document Analysis and Recognition (IJDAR)
  2. International Journal of Document Analysis and Recognition (IJDAR) : Volume 19
  3. International Journal of Document Analysis and Recognition (IJDAR) : Volume 19, Issue 3, September 2016
  4. An evidence-based model of saliency feature extraction for scene text analysis
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International Journal of Document Analysis and Recognition (IJDAR) : Volume 20
International Journal of Document Analysis and Recognition (IJDAR) : Volume 19
International Journal of Document Analysis and Recognition (IJDAR) : Volume 19, Issue 4, December 2016
International Journal of Document Analysis and Recognition (IJDAR) : Volume 19, Issue 3, September 2016
Page segmentation using minimum homogeneity algorithm and adaptive mathematical morphology
Music staff removal with supervised pixel classification
A knowledge-based recognition system for historical Mongolian documents
Discovering similar Chinese characters in online handwriting with deep convolutional neural networks
Online recognition of sketched arrow-connected diagrams
An evidence-based model of saliency feature extraction for scene text analysis
International Journal of Document Analysis and Recognition (IJDAR) : Volume 19, Issue 2, June 2016
International Journal of Document Analysis and Recognition (IJDAR) : Volume 19, Issue 1, March 2016
International Journal of Document Analysis and Recognition (IJDAR) : Volume 18
International Journal of Document Analysis and Recognition (IJDAR) : Volume 17
International Journal of Document Analysis and Recognition (IJDAR) : Volume 16
International Journal of Document Analysis and Recognition (IJDAR) : Volume 15
International Journal of Document Analysis and Recognition (IJDAR) : Volume 14
International Journal of Document Analysis and Recognition (IJDAR) : Volume 13
International Journal of Document Analysis and Recognition (IJDAR) : Volume 12
International Journal of Document Analysis and Recognition (IJDAR) : Volume 11
International Journal of Document Analysis and Recognition (IJDAR) : Volume 10
International Journal of Document Analysis and Recognition (IJDAR) : Volume 9
International Journal of Document Analysis and Recognition (IJDAR) : Volume 8
International Journal of Document Analysis and Recognition (IJDAR) : Volume 7
International Journal of Document Analysis and Recognition (IJDAR) : Volume 6
International Journal of Document Analysis and Recognition (IJDAR) : Volume 5
International Journal of Document Analysis and Recognition (IJDAR) : Volume 4
International Journal of Document Analysis and Recognition (IJDAR) : Volume 3
International Journal of Document Analysis and Recognition (IJDAR) : Volume 2
International Journal of Document Analysis and Recognition (IJDAR) : Volume 1

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An evidence-based model of saliency feature extraction for scene text analysis

Content Provider Springer Nature Link
Author Chen, Yui Lang Yu, Pao Ta
Copyright Year 2016
Abstract Saliency text is that characters are ordered with visibility and expressivity. It also contains important clues for video analysis, indexing, and retrieval. Thus, in order to localize the saliency text, a critical stage is to collect key points from real text pixels. In this paper, we propose an evidence-based model of saliency feature extraction (SFE) to probe saliency text points (STPs), which have strong text signal structure in multi-observations simultaneously and always appear between text and its background. Through the multi-observations, each signal structure with rhythms of signal segments is extracted at every location in the visual field. It supports source of evidences for our evidence-based model, where evidences are measured to effectively estimate the degrees of plausibility for obtaining the STP. The evaluation results on benchmark datasets also demonstrate that our proposed approach achieves the state-of-the-art performance on exploring real text pixels and significantly outperforms the existing algorithms for detecting text candidates. The STPs can be the extremely reliable text candidates for future text detectors.
Starting Page 269
Ending Page 287
Page Count 19
File Format PDF
ISSN 14332833
Journal International Journal of Document Analysis and Recognition (IJDAR)
Volume Number 19
Issue Number 3
e-ISSN 14332825
Language English
Publisher Springer Berlin Heidelberg
Publisher Date 2016-07-21
Publisher Place Berlin, Heidelberg
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Saliency text Text signal Evidence theory Text feature Plausibility Text detection Image Processing and Computer Vision Pattern Recognition
Content Type Text
Resource Type Article
Subject Computer Science Applications Computer Vision and Pattern Recognition Software
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