RSLDI: Restoration of single-sided low-quality document images

This paper addresses the problem of enhancing and restoring single-sided low-quality single-sided document images. Initially, a series of multi-level classifiers is introduced covering several levels, including the regional and content levels. These classifiers can then be integrated into any enhancement or restoration method to generalize or improve them. Based on these multi-level classifiers, we first propose a novel PDE-based method for the restoration of the degradations in single-sided document images. To reduce the local nature of PDE-based methods, we empower our method with two flow fields to play the role of regional classifiers and help in preserving meaningful pixels. Also, the new method further diffuses the background information by using a content classifier, which provides an efficient and accurate restoration of the degraded backgrounds. The performance of the method is tested on both real samples, from the Google Book Search dataset, UNESCO's Memory of the World Programme, and the Juma Al Majid (Dubai) datasets, and synthesized samples provided by our degradation model. The results are promising. The method-independent nature of the classifiers is illustrated by modifying the ICA method to make it applicable to single-sided documents, and also by providing a Bayesian binarization model.