A Unified Framework Based on the Level Set Approach for Segmentation of Unconstrained Double-Sided Document Images Suffering from Bleed-Through

Authors:
Reza Farrahi Moghaddam;David Rivest-Henault;Itay Bar-Yosef;Mohamed Cheriet
Affiliations:
-;-;-;-
Venue:
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Year:
2009

Citing 0
Cited 4

A multi-scale framework for adaptive binarization of degraded document images

Pattern Recognition
Enhancement of historical printed document images by combining Total Variation regularization and Non-local Means filtering

Image and Vision Computing
Nonlinear model and constrained ML for removing back-to-front interferences from recto-verso documents

Pattern Recognition
A real-world noisy unstructured handwritten notebook corpus for document image analysis research

Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data

Quantified Score

Hi-index	0.01

Visualization

Abstract

A novel method for the segmentation of double-sided ancient document images suffering from bleed-through effect is presented. It takes advantage of the level set framework to provide a completely integrated process for the segmentation of the text along with the removal of the bleed-through interfering patterns. This process is driven by three forces: 1) a binarization force based on an adaptive global threshold is used to identify region of low intensity, 2) a reverse diffusion force allows for the separation of interfering patterns from the true text, and 3) a small regularization force favors smooth boundaries. This integrated method achieves high quality results at reasonable computational cost, and can easily host other concepts to enhance its performance. The method is successfully applied to real and synthesized degraded document images. Also, the registration problem of the double-sided document images is addressed by introducing a level set method; the results are promising.