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

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  • 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Optical Character Recognition (OCR) can open up understudied historical documents to computational analysis, but the accuracy of OCR software varies.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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. 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).
    2. Correia, Sergio & Luck, Stephan, 2023. "Digitizing historical balance sheet data: A practitioner’s guide," Explorations in Economic History, Elsevier, vol. 87(C).
    3. 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.

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