Markov random field models in computer vision
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning to Detect Scene Text Using a Higher-Order MRF with Belief Propagation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 6 - Volume 06
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Text Locating Competition Results
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Incremental learning of object detectors using a visual shape alphabet
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Object count/area graphs for the evaluation of object detection and segmentation algorithms
International Journal on Document Analysis and Recognition
A Contour-Based Robust Algorithm for Text Detection in Color Images
IEICE - Transactions on Information and Systems
Color text extraction with selective metric-based clustering
Computer Vision and Image Understanding
Word Image Decomposition from Mixed Text/Graphics Images Using Statistical Methods
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04
Character-Stroke Detection for Text-Localization and Extraction
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Object Recognition by Integrating Multiple Image Segmentations
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Scene Text Extraction Using Focus of Mobile Camera
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Object detection using spatial histogram features
Image and Vision Computing
Regularized margin-based conditional log-likelihood loss for prototype learning
Pattern Recognition
Scene Text Extraction with Edge Constraint and Text Collinearity
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A method for text localization and recognition in real-world images
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
ICDAR 2011 Robust Reading Competition Challenge 2: Reading Text in Scene Images
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Text Localization in Real-World Images Using Efficiently Pruned Exhaustive Search
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Efficient belief propagation with learned higher-order markov random fields
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Automatic text detection and tracking in digital video
IEEE Transactions on Image Processing
A Hybrid Approach to Detect and Localize Texts in Natural Scene Images
IEEE Transactions on Image Processing
Text String Detection From Natural Scenes by Structure-Based Partition and Grouping
IEEE Transactions on Image Processing
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Text contained in scene images provides the semantic context of the images. For that reason, robust extraction of text regions is essential for successful scene text understanding. However, separating text pixels from scene images still remains as a challenging issue because of uncontrolled lighting conditions and complex backgrounds. In this paper, we propose a two-stage conditional random field (TCRF) approach to robustly extract text regions from the scene images. The proposed approach models the spatial and hierarchical structures of the scene text, and it finds text regions based on the scene text model. In the first stage, the system generates multiple character proposals for the given image by using multiple image segmentations and a local CRF model. In the second stage, the system selectively integrates the generated character proposals to determine proper character regions by using a holistic CRF model. Through the TCRF approach, we cast the scene text separation problem as a probabilistic labeling problem, which yields the optimal label configuration of pixels that maximizes the conditional probability of the given image. Experimental results indicate that our framework exhibits good performance in the case of the public databases.