Progress in Camera-Based Document Image Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Machine Printed Text and Handwriting Identification in Noisy Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
OCR binarization and image pre-processing for searching historical documents
Pattern Recognition
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A novel ring radius transform for video character reconstruction
Pattern Recognition
Markov random fields for improving 3D mesh analysis and segmentation
EG 3DOR'08 Proceedings of the 1st Eurographics conference on 3D Object Retrieval
Computing precision and recall with missing or uncertain ground truth
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
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Binarization techniques have been developed in the document analysis community for over 30 years and many algorithms have been used successfully. On the other hand, document analysis tasks are more and more frequently being applied to multimedia documents such as video sequences. Due to low resolution and lossy compression, the binarization of text included in the frames is a non trivial task. Existing techniques work without a model of the spatial relationships in the image, which makes them less powerful. We introduce a new technique based on a Markov Random Field (MRF) model of the document. The model parameters (clique potentials) are learned from training data and the binary image is estimated in a Bayesian framework. The performance is evaluated using commercial OCR software.