IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v5y2022i1d10.1007_s42001-021-00149-1.html
   My bibliography  Save this article

OCR with Tesseract, Amazon Textract, and Google Document AI: a benchmarking experiment

Author

Listed:
  • Thomas Hegghammer

    (Norwegian Defence Research Establishment (FFI))

Abstract

Optical Character Recognition (OCR) can open up understudied historical documents to computational analysis, but the accuracy of OCR software varies. This article reports a benchmarking experiment comparing the performance of Tesseract, Amazon Textract, and Google Document AI on images of English and Arabic text. English-language book scans (n = 322) and Arabic-language article scans (n = 100) were replicated 43 times with different types of artificial noise for a corpus of 18,568 documents, generating 51,304 process requests. Document AI delivered the best results, and the server-based processors (Textract and Document AI) performed substantially better than Tesseract, especially on noisy documents. Accuracy for English was considerably higher than for Arabic. Specifying the relative performance of three leading OCR products and the differential effects of commonly found noise types can help scholars identify better OCR solutions for their research needs. The test materials have been preserved in the openly available “Noisy OCR Dataset” (NOD) for reuse in future benchmarking studies.

Suggested Citation

  • Thomas Hegghammer, 2022. "OCR with Tesseract, Amazon Textract, and Google Document AI: a benchmarking experiment," Journal of Computational Social Science, Springer, vol. 5(1), pages 861-882, May.
    • Handle: RePEc:spr:jcsosc:v:5:y:2022:i:1:d:10.1007_s42001-021-00149-1
    DOI: 10.1007/s42001-021-00149-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-021-00149-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s42001-021-00149-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Costin-Anton Boiangiu & Radu Ioanitescu & Razvan-Costin Dragomir, 2016. "Voting-Based Ocr System," Romanian Economic Business Review, Romanian-American University, vol. 10(2), pages 470-486, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Grzegorz Szyjewski, 2023. "Securing Digital Copies of the Documents to Ensure Documents' Integrity," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 718-726.
    2. Patel, Ashish Singh & Tiwari, Vivek & Ojha, Muneendra & Vyas, O.P., 2023. "Ontology-based detection and identification of complex event of illegal parking using SPARQL and description logic queries," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    3. Correia, Sergio & Luck, Stephan, 2023. "Digitizing historical balance sheet data: A practitioner’s guide," Explorations in Economic History, Elsevier, vol. 87(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcsosc:v:5:y:2022:i:1:d:10.1007_s42001-021-00149-1. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.