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• W2024387429 abstract "In this paper we define a bidimensional extension of stochastic context-free grammars for structure detection and segmentation of images of documents. Two sets of text classification features are used to perform an initial classification of each zone of the page. Then, the document segmentation is obtained as the most likely hypothesis according to a stochastic grammar. We used a dataset of historical marriage license books to validate this approach. We also tested several inference algorithms for probabilistic graphical models and the results showed that the proposed grammatical model outperformed the other methods. Furthermore, grammars also provide the document structure along with its segmentation." @default.
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• W2024387429 date "2015-02-01" @default.
• W2024387429 modified "2023-09-26" @default.
• W2024387429 title "Structure detection and segmentation of documents using 2D stochastic context-free grammars" @default.
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• W2024387429 doi "https://doi.org/10.1016/j.neucom.2014.08.076" @default.
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