DL '97 Proceedings of the second ACM international conference on Digital libraries
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text Identification in Noisy Document Images Using Markov Random Field
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
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
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Finding Text in Natural Scenes by Figure-Ground Segmentation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Detecting and reading text in natural scenes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Automatic text detection and tracking in digital video
IEEE Transactions on Image Processing
Figure-ground segmentation using factor graphs
Image and Vision Computing
Adaptive Deblurring for Camera-Based Document Image Processing
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Camera-Based signage detection and recognition for blind persons
ICCHP'12 Proceedings of the 13th international conference on Computers Helping People with Special Needs - Volume Part II
Fuzzy graph modeling for text segmentation from land map images
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
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We introduce a new framework for feature grouping based on factor graphs, which are graphical models that encode interactions among arbitrary numbers of random variables. The ability of factor graphs to express interactions higher than pairwise order (the highest order encountered in most graphical models used in computer vision) is useful for modeling a variety of pattern recognition problems. In particular, we show how this property makes factor graphs a natural framework for performing grouping and segmentation, which we apply to the problem of finding text in natural scenes. We demonstrate an implementation of our factor graph-based algorithm for finding text on a Nokia camera phone, which is intended for eventual use in a camera phone system that finds and reads text (such as street signs) in natural environments for blind users.