Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
The Document Spectrum for Page Layout Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Separating Handwritten Material from Machine Printed Text Using Hidden Markov Models
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Machine Printed Text and Handwriting Identification in Noisy Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative Figure-Ground Discrimination
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Multilevel Belief Propagation for Fast Inference on Markov Random Fields
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Combined Top-Down/Bottom-Up Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Based Text Identification from Annotated Machine Printed Documents
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Using a boosted tree classifier for text segmentation in hand-annotated documents
Pattern Recognition Letters
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Separating machine printed text and handwriting from overlapping text is a challenging problem in the document analysis field and no reliable algorithms have been developed thus far. In this paper, we propose a novel approach for separating handwriting from binary image of overlapped text. Instead of using fixed size training patches, we describe an aggregation method which uses shape context features to extract training samples automatically. We use a Markov Random Field (MRF) to model the overlapped text. The neighbor system is inherited from a coarsening procedure and the prior and likelihood of the MRF is learned based on a distance metric. Experimental results show that the proposed method can achieve 87.97% recall for handwriting and 91.44% recall for machine printed text.