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Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering

Annals of Computer Science and Information Systems, Volume 33

Improving Logical Structure Analysis of Visually Structured Documents with Textual Features

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DOI: http://dx.doi.org/10.15439/2022R26

Citation: Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 33, pages 151–156 ()

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Abstract. This paper introduces a new model to improve the quality of logical structure analysis of visually structured documents. To do that, we extend the model of Koreeda and Manning. In order to enhance textual features, we define a new feature that uses the font size of texts as an indicator. As our observation, the font size is an important indicator that can be used to represent the structure of a document. The new font size feature is combined with visual, textual, and semantic features for training an analyzer. Experimental results on four legal datasets show that the new font size feature contributes to the model and helps to improve the F-scores. The ablation study also shows the contribution of each feature in our model.

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