Document page structure learning for fixed-layout e-books using conditional random fields

Xin Tao; Zhi Tang; Canhui Xu

doi:10.1117/12.2039492

24 March 2014 Document page structure learning for fixed-layout e-books using conditional random fields

Xin Tao, Zhi Tang, Canhui Xu

Proceedings Volume 9021, Document Recognition and Retrieval XXI; 90210I (2014) https://doi.org/10.1117/12.2039492
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States

Abstract

In this paper, a model is proposed to learn logical structure of fixed-layout document pages by combining support vector machine (SVM) and conditional random fields (CRF). Features related to each logical label and their dependencies are extracted from various original Portable Document Format (PDF) attributes. Both local evidence and contextual dependencies are integrated in the proposed model so as to achieve better logical labeling performance. With the merits of SVM as local discriminative classifier and CRF modeling contextual correlations of adjacent fragments, it is capable of resolving the ambiguities of semantic labels. The experimental results show that CRF based models with both tree and chain graph structures outperform the SVM model with an increase of macro-averaged F₁ by about 10%.

Citation Download Citation

Xin Tao, Zhi Tang, and Canhui Xu "Document page structure learning for fixed-layout e-books using conditional random fields", Proc. SPIE 9021, Document Recognition and Retrieval XXI, 90210I (24 March 2014); https://doi.org/10.1117/12.2039492

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