Bayesian damage recognition in document images based on a joint global and local homogeneity model

[摘要] Physical damages (such as torn-offs and scratches) are commonly seen in historical documents. Recognition of such damages is currently absent in digitization-and-information-extraction (DIE) systems but crucial for automatic document comprehension and exploitation. In this paper we propose a generic damage recognition (DR) method based on a joint global and local modeling of the text homogeneity (TH) pattern exhibited in document images. More specifically, a connected component (CC) based formulation is developed as a global homogeneity measure, where TH is characterized using a probabilistic graph model for a coarse recognition of damaged regions. A multi-resolution analysis (MRA) of TH is further developed for a granular within-CC recognition of damage pixels, where the disparity between damage and text pixels is characterized by exploiting neighborhood transitions. This enables the formulation of a local homogeneity measure, where the neighborhood transition around an individual pixel is modeled using the propagation of the approximation coefficients of a stationary wavelet transform (SWT). The proposed global and local homogeneity measures are integrated as a joint likelihood in a Bayesian model with a Markov random field (MRF) prior, where DR is formulated as a maximum a posterior (MAP) inference which is addressed using Markov Chain Monte Carlo (MCMC) sampling. The resulting algorithm is tested on a set of real-life historical newspaper images containing damages of varying size and shape. The performance of the algorithm is evaluated using both F-measures and the Intersection-over-Union (IoU) metric, where test results demonstrate the promising potential of the proposed method. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

[发布日期] 2021-10-01

[关键词] Damage recognition;Text homogeneity;Neighborhood transition;Propagation of wavelet approximation;Bayesian inference