Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
412128 | Neurocomputing | 2015 | 8 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Francisco Álvaro, Francisco Cruz, Joan-Andreu Sánchez, Oriol Ramos Terrades, José-Miguel Benedí,