Nested state indexing in pairwise Markov networks for fast handwritten document image rule-line removal

Authors:
Huaigu Cao;Rohit Prasad;Premkumar Natarajan;Venu Govindaraju
Affiliations:
BBN Technologies, Cambridge, MA;BBN Technologies, Cambridge, MA;BBN Technologies, Cambridge, MA;CUBS, University at Buffalo, Amherst, NY
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 7
Cited 0

Binarization of Low Quality Text Using a Markov Random Field Model

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Learning Low-Level Vision

ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Generalizing Swendsen-Wang to Sampling Arbitrary Posterior Probabilities

IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Models for Pattern Recognition: From Theory to Applications

Markov Models for Pattern Recognition: From Theory to Applications
Preprocessing of Low-Quality Handwritten Documents Using Markov Random Fields

IEEE Transactions on Pattern Analysis and Machine Intelligence
A computational model for causal and diagnostic reasoning in inference systems

IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Multi-lingual offline handwriting recognition using hidden Markov models: a script-independent approach

SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

The Markov random field (MRF) has been applied to modeling the connectivity constraints of the text in document images for tasks like binarization and rule-line removal. One challenge of applying the MRF is its high computational cost. This paper presents a method using two nested set of states trained to reduce the computational cost of patch-based MRF. The two sets of states are trained at different levels in coarseto-fine order. We show effective reduction of run time but very little loss of quality using rule-line removal experiments.