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Analyzing the Content of Business Documents Recognized with a Large Number of Errors Using Modified Levenshtein Distance

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Cyber-Physical Systems: Intelligent Models and Algorithms

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 417))

Abstract

The chapter discusses methods for analyzing test material of business documents, lists tasks that use word comparison. The features of the analysis of recognized texts are indicated. A mechanism for identifying recognized words based on textual feature points is described. The advantages and disadvantages of Levenshtein position are listed. Other distances between string objects are described: Jaro-Winkler similarity, multiset metric, MFKC metric. The Levenshtein standard distance is compared to other distances between two string objects. A modification of the Levenshtein position is proposed focused on the features of the recognized characters. Experimental results are presented that demonstrate the effect of using the proposed distance in comparison with the normalized Levenshtein distances. The experiments investigate the extraction of data from the document and the classification of documents. We also compared the time spent on calculating the modified Levenshtein metric and the multiset metric. The proposed method can be applied in a modern CAD system in the recognition component to analyze the information of recognized text documents. Also, the method can be in the system of the analysis of the recognized text using the methods of computational linguistics.

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Acknowledgements

The research is carried out with partial financial support of The Russian Foundation for Basic Research (Project 20-07-00934).

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Authors and Affiliations

  1. Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 9 Prospect 60-Letiya Oktyabrya, Moscow, 117312, Russia

    Oleg Slavin & Vera Farsobina

  2. Moscow Institute of Physics and Technology (National Research University), Moscow Region, 9 Institutskiy Per., Dolgoprudny, 141701, Russia

    Oleg Slavin

  3. Smart Engines Service, 9 Prospect 60-Letiya Oktyabrya, Moscow, 117312, Russia

    Oleg Slavin & Aleksei Myshev

  4. National Research Nuclear University MEPHI, Moscow, Russia

    Oleg Slavin, Vera Farsobina & Aleksei Myshev

Authors
  1. Oleg Slavin
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  2. Vera Farsobina
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  3. Aleksei Myshev
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Corresponding author

Correspondence to Oleg Slavin .

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Editors and Affiliations

  1. r.B-1406, Volgograd State Technical University, Volgograd, Russia

    Alla G. Kravets

  2. Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia

    Alexander A. Bolshakov

  3. Apt 8, Volgograd State Technical University, Volgograd, Russia

    Maxim Shcherbakov

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Slavin, O., Farsobina, V., Myshev, A. (2022). Analyzing the Content of Business Documents Recognized with a Large Number of Errors Using Modified Levenshtein Distance. In: Kravets, A.G., Bolshakov, A.A., Shcherbakov, M. (eds) Cyber-Physical Systems: Intelligent Models and Algorithms. Studies in Systems, Decision and Control, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-95116-0_22

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  • DOI: https://doi.org/10.1007/978-3-030-95116-0_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95115-3

  • Online ISBN: 978-3-030-95116-0

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