Visual information extraction

  • Authors:

  • Yonatan Aumann;Ronen Feldman;Yair Liberzon;Benjamin Rosenfeld;Jonathan Schler

  • Affiliations:

  • Department of Computer Science, Bar Ilan University, 52900, Ramat Gan, Israel and ClearForest Ltd., 6 Yoni Netanyahu Street, 60376, Yehuda, Israel;Department of Computer Science, Bar Ilan University, 52900, Ramat Gan, Israel and ClearForest Ltd., 6 Yoni Netanyahu Street, 60376, Yehuda, Israel;ClearForest Ltd., 6 Yoni Netanyahu Street, 60376, Yehuda, Israel;ClearForest Ltd., 6 Yoni Netanyahu Street, 60376, Yehuda, Israel;Department of Computer Science, Bar Ilan University, 52900, Ramat Gan, Israel

  • Venue:
  • Knowledge and Information Systems
  • Year:
  • 2006

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Abstract

Typographic and visual information is an integral part of textual documents. Most information extraction (IE) systems ignore most of this visual information, processing the text as a linear sequence of words. Thus, much valuable information is lost. In this paper, we show how to make use of this visual information for IE. We present an algorithm that allows to automatically extract specific fields of the document (such as the title, author, etc.) based exclusively on the visual formatting of the document, without any reference to the semantic content. The algorithm employs a machine learning approach, whereby the system is first provided with a set of training documents in which the target fields are manually tagged and automatically learns how to extract these fields in future documents. We implemented the algorithm in a system for automatic analysis of documents in PDF format. We present experimental results of applying the system on a set of financial documents, extracting nine different target fields. Overall, the system achieved a 90% accuracy.