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
Similarity-based training set acquisition for continuous handwriting recognition
Information Sciences: an International Journal
Model-based ruling line detection in noisy handwritten documents
Pattern Recognition Letters
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This paper presents a statistical approach to the preprocessing of degraded handwritten forms including the steps of binarization and form line removal. The degraded image is modeled by a Markov Random Field (MRF) where the hidden-layer prior probability is learned from a training set of high-quality binarized images and the observation probability density is learned on-the-fly from the gray-level histogram of the input image. We have modified the MRF model to drop the preprinted ruling lines from the image. We use the patch-based topology of the MRF and Belief Propagation (BP) for efficiency in processing. To further improve the processing speed, we prune unlikely solutions from the search space while solving the MRF. Experimental results show higher accuracy on two data sets of degraded handwritten images than previously used methods.