Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Word Spotting: A New Approach to Indexing Handwriting
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Document zone content classification and its performance evaluation
Pattern Recognition
Text line segmentation of historical documents: a survey
International Journal on Document Analysis and Recognition
Spatial and Spectral Based Segmentation of Text in Multispectral Images of Ancient Documents
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Document image segmentation using discriminative learning over connected components
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
User-assisted alignment of Arabic historical manuscripts
Proceedings of the 2011 Workshop on Historical Document Imaging and Processing
Learning and incorporating top-down cues in image segmentation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Fast anisotropic Gauss filtering
IEEE Transactions on Image Processing
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Layout Analysis for Arabic Historical Document Images Using Machine Learning
ICFHR '12 Proceedings of the 2012 International Conference on Frontiers in Handwriting Recognition
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We present a method to segment historical document images into regions of different content. First, we segment text elements from non-text elements using a binarized version of the document. Then, we refine the segmentation of the non-text regions into drawings, background and noise. At this stage, spatial and color features are exploited to guarantee coherent regions in the final segmentation. Experiments show that the suggested approach achieves better segmentation quality with respect to other methods. We examine the segmentation quality on 252 pages of a historical manuscript, for which the suggested method achieves about 92% and 90% segmentation accuracy of drawings and text elements, respectively.