Probabilistic homogeneity for document image segmentation
نویسندگان
چکیده
In this paper we propose a novel probabilistic framework for document segmentation exploiting human perceptual recognition of text regions from complicated layouts. particular, conceptualize homogeneity as the Gestalt pattern displayed in regions, characterized by proximately and symmetrically arranged units with similar morphological texture features. We model local region connected component (CC) using an hierarchical formulation, which simulates random walk-and-check on graph encoding neighborhood CC. The proposed formulation allows effective computation what call (PLTH) weighted summation weights graph, are derived description between neighboring CCs computed through Bayesian cue integration. PLTH enables multi-aspect analysis, where various primitives such geometrical configuration, features, characterization location priors integrated one computational model. This non-text classification preceding any grouping process, is currently absent segmentation. Experimental results show that our method based improves upon state-of-the-art.
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ژورنال
عنوان ژورنال: Pattern Recognition
سال: 2021
ISSN: ['1873-5142', '0031-3203']
DOI: https://doi.org/10.1016/j.patcog.2020.107591