Conference Paper

Mathematical Formula Identification in PDF Documents

DOI: 10.1109/ICDAR.2011.285 Conference: 2011 International Conference on Document Analysis and Recognition, ICDAR 2011, Beijing, China, September 18-21, 2011
Source: DBLP


Recognizing mathematical expressions in PDF documents is a new and important field in document analysis. It is quite different from extracting mathematical expressions in image-based documents. In this paper, we propose a novel method by combining rule-based and learning-based methods to detect both isolated and embedded mathematical expressions in PDF documents. Moreover, various features of formulas, including geometric layout, character and context content, are used to adapt to a wide range of formula types. Experimental results show satisfactory performance of the proposed method. Furthermore, the method has been successfully incorporated into a commercial software package for large-scale Chinese e-Book production.

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Available from: Xiaoyan Lin, Apr 18, 2014
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    • "Surprisingly, there is very little existing work on how best to realize this process. Lines of research most closely related to the present work include extracting numerical attributes (e.g., [1] [4]), supporting numerical document queries (e.g., [5] [12]), and formula identification (e.g., [7]). However, none of these existing works address the comprehensive extraction of and search for measured information in document data, as described above. "
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    • "Formulas and algorithms must be determined and restored through progressive aggregation of the low-level components in the obstacles section, because the higher-level block s of the layout section might mix them with normal running text of the document paragraphs. Inspired by [3], we look for peculiar elements in the document and group them into consistent aggregates: images of very small size overlapping to text blocks (as potential symbols), strokes (e.g., denoting ratios and roots), box es whose text suggests the presence of mathematics or code, and so on. More specifically, we define the following classes: "
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    • "In addition, most documents in the existing datasets were published too early to obtain the corresponding source PDF documents. As a result, for mathematical formula recognition methods focused on PDF documents [9] [10] [11], it is difficult to compare the performance directly with image-based methods. "
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    ABSTRACT: This paper presents a performance evaluation system for mathematical formula identification. First, a ground-truth dataset is constructed to facilitate the performance comparison of different mathematical formula identification algorithms. Statistics analysis of the dataset shows the diversities of the dataset to reflect the real-world documents. Second, a performance evaluation metric for mathematical formula identification is proposed, including the error type definitions and the scenario-adjustable scoring. The proposed metric enables in-depth analysis of mathematical formula identification systems in different scenarios. Finally, based on the proposed evaluation metric, a tool is developed to automatically evaluate mathematical formula identification results. It is worth noting that the ground-truth dataset and the evaluation tool are freely available for academic purpose.
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