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Information extraction by finding repeated structure

Published: 09 June 2010 Publication History

Abstract

Repetition of layout structure is prevalent in document images. In document design, such repetition conveys the underlying logical and functional structure of the data. For example, in invoices, the names, unit prices, quantities and other descriptors of every line item are laid out in a consistent spatial structure. We propose a general method for extracting such repeated structure from documents. After receiving a single example of the structure to be found, the proposed method localizes additional instances of this structure in the same document and in additional documents. A wide variety of perceptually motivated cues (such as alignment and saliency) is used for this purpose. These cues are combined in a probabilistic model, and a novel algorithm for exact inference in this model is proposed and used. We demonstrate that this method can cope with complex instances of repeated structure and generalizes successfully across a wide range of structure variations.

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Cited By

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  • (2024)SGFNet: A semantic graph-based multimodal network for financial invoice information extractionExpert Systems with Applications10.1016/j.eswa.2024.125156(125156)Online publication date: Aug-2024
  • (2021)Who is Selling to Whom – Feature Evaluation for Multi-block Classification in Invoice Information ExtractionSpeech and Computer10.1007/978-3-030-87802-3_23(250-261)Online publication date: 22-Sep-2021
  • (2021)LAMBERT: Layout-Aware Language Modeling for Information ExtractionDocument Analysis and Recognition – ICDAR 202110.1007/978-3-030-86549-8_34(532-547)Online publication date: 2-Sep-2021
  • Show More Cited By

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cover image ACM Other conferences
DAS '10: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
June 2010
490 pages
ISBN:9781605587738
DOI:10.1145/1815330
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 June 2010

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Author Tags

  1. information extraction
  2. repeated structure

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Cited By

View all
  • (2024)SGFNet: A semantic graph-based multimodal network for financial invoice information extractionExpert Systems with Applications10.1016/j.eswa.2024.125156(125156)Online publication date: Aug-2024
  • (2021)Who is Selling to Whom – Feature Evaluation for Multi-block Classification in Invoice Information ExtractionSpeech and Computer10.1007/978-3-030-87802-3_23(250-261)Online publication date: 22-Sep-2021
  • (2021)LAMBERT: Layout-Aware Language Modeling for Information ExtractionDocument Analysis and Recognition – ICDAR 202110.1007/978-3-030-86549-8_34(532-547)Online publication date: 2-Sep-2021
  • (2020)Cardinal Graph Convolution Framework for Document Information ExtractionProceedings of the ACM Symposium on Document Engineering 202010.1145/3395027.3419584(1-11)Online publication date: 29-Sep-2020
  • (2020)Automatic Information Extraction from Scanned DocumentsSpeech and Computer10.1007/978-3-030-60276-5_9(87-96)Online publication date: 29-Sep-2020
  • (2019)A Collaborative Framework for Structure Identification over Print DocumentsProceedings of the Workshop on Human-In-the-Loop Data Analytics10.1145/3328519.3329131(1-8)Online publication date: 5-Jul-2019
  • (2019)EATEN: Entity-Aware Attention for Single Shot Visual Text Extraction2019 International Conference on Document Analysis and Recognition (ICDAR)10.1109/ICDAR.2019.00049(254-259)Online publication date: Sep-2019
  • (2018)Recognition of OCR Invoice Metadata Block TypesText, Speech, and Dialogue10.1007/978-3-030-00794-2_33(304-312)Online publication date: 8-Sep-2018
  • (2015)g-DICEInternational Journal on Document Analysis and Recognition10.1007/s10032-015-0253-z18:4(337-355)Online publication date: 1-Dec-2015
  • (2013)Cooperative and Fast-Learning Information Extraction from Business Documents for Document ArchivingOn the Move to Meaningful Internet Systems: OTM 2013 Workshops10.1007/978-3-642-41033-8_4(22-31)Online publication date: 2013
  • Show More Cited By

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