Structure detection and segmentation of documents using 2D stochastic context-free grammars

NEUROCOMPUTING

卷:150

Article; Proceedings Paper

Alvaro, Francisco¹  Cruz, Francisco²  Sanchez, Joan-Andreu¹  Terrades, Oriol Ramos²  Benedi, Jose-Miguel¹ 

[1] Univ Politecn Valencia, Valencia, Spain

[2] Univ Autonoma Barcelona, Ctr Visio Computador, E-08193 Barcelona, Spain

关键词: Document image analysis;    Stochastic context-free grammars;    Text classification features;   

DOI  :  10.1016/j.neucom.2014.08.076

来源: Elsevier

【 摘 要 】

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. (C) 2014 Elsevier B.V. All rights reserved.

【 授权许可】

Free