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Efficient OCR for Building a Diverse Digital History

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  • Jacob Carlson
  • Tom Bryan
  • Melissa Dell

Abstract

Thousands of users consult digital archives daily, but the information they can access is unrepresentative of the diversity of documentary history. The sequence-to-sequence architecture typically used for optical character recognition (OCR) - which jointly learns a vision and language model - is poorly extensible to low-resource document collections, as learning a language-vision model requires extensive labeled sequences and compute. This study models OCR as a character level image retrieval problem, using a contrastively trained vision encoder. Because the model only learns characters' visual features, it is more sample efficient and extensible than existing architectures, enabling accurate OCR in settings where existing solutions fail. Crucially, the model opens new avenues for community engagement in making digital history more representative of documentary history.

Suggested Citation

  • Jacob Carlson & Tom Bryan & Melissa Dell, 2023. "Efficient OCR for Building a Diverse Digital History," Papers 2304.02737, arXiv.org, revised Jul 2024.
  • Handle: RePEc:arx:papers:2304.02737
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    File URL: http://arxiv.org/pdf/2304.02737
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    References listed on IDEAS

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    1. Beach, Brian & Hanlon, W. Walker, 2022. "Historical Newspaper Data: A Researcher's Guide and Toolkit," CEPR Discussion Papers 17366, C.E.P.R. Discussion Papers.
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    Cited by:

    1. Xinmei Yang & Abhishek Arora & Shao-Yu Jheng & Melissa Dell, 2023. "Quantifying Character Similarity with Vision Transformers," Papers 2305.14672, arXiv.org.
    2. Melissa Dell & Jacob Carlson & Tom Bryan & Emily Silcock & Abhishek Arora & Zejiang Shen & Luca D'Amico-Wong & Quan Le & Pablo Querubin & Leander Heldring, 2023. "American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspapers," Papers 2308.12477, arXiv.org.

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