Robust Text Detection in Natural Scene Images

Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks. In this paper, we propose an accurate and robust method for detecting texts in natural scene images. A fast and effective pruning algorithm is designed to extract Maximally Stable Extrema...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 36; no. 5; pp. 970 - 983
Main Authors: Yin, Xu-Cheng, Yin, Xuwang, Huang, Kaizhu, Hao, Hong-Wei
Format: Journal Article
Language:English
Published: Los Alamitos, CA IEEE 01.05.2014
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithm design and analysis
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Artificial intelligence
Classifiers
Clustering
Clustering algorithms
Computer science; control theory; systems
Computing Methodologies
Data processing. List processing. Character string processing
Educational institutions
Exact sciences and technology
Image detection
Image Processing and Computer Vision
Intelligence
Measurement
Memory organisation. Data processing
Pattern analysis
Pattern recognition. Digital image processing. Computational geometry
Robustness
Scene Analysis
Software
Text processing
Texts
Theoretical computing
Vegetation
single-link clustering
Scene text detection
maximally stable extremal regions
distance metric learning
Cluster analysis
Competition
Image processing
Image content
Task analysis
Extremum
Classification
Natural scenes
Pruning(tree)
Fast algorithm
Document analysis
Distance
Posterior distribution
Content analysis
Self learning
Computer vision
Data analysis
Multilingualism
Probabilistic approach
Internet protocol
Cluster
Information retrieval
Text
Interest region
Multiple view
Image segmentation
Posterior probability
Optical character recognition
Scene analysis
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:Get full text
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Description
Summary:Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks. In this paper, we propose an accurate and robust method for detecting texts in natural scene images. A fast and effective pruning algorithm is designed to extract Maximally Stable Extremal Regions (MSERs) as character candidates using the strategy of minimizing regularized variations. Character candidates are grouped into text candidates by the single-link clustering algorithm, where distance weights and clustering threshold are learned automatically by a novel self-training distance metric learning algorithm. The posterior probabilities of text candidates corresponding to non-text are estimated with a character classifier; text candidates with high non-text probabilities are eliminated and texts are identified with a text classifier. The proposed system is evaluated on the ICDAR 2011 Robust Reading Competition database; the f-measure is over 76%, much better than the state-of-the-art performance of 71%. Experiments on multilingual, street view, multi-orientation and even born-digital databases also demonstrate the effectiveness of the proposed method.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2013.182