Efficient OCR for Building a Diverse Digital History
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Abstract (arXiv)
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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 38 of its 64 references that are in the map. First posted on arXiv 5 Apr 2023.
Builds on
38 of 64 references.
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26 more
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Built on by
Citing papers per year: 2023: 4; 2024: 2; 2025: 3; 2026: 1.
10 papers.
- 2023American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspaperscited by 11
- 2023EfficientOCR: An Extensible, Open-Source Package for Efficiently Digitizing World Knowledgecited by 5
- 2024Newswire: A Large-Scale Structured Database of a Century of Historical Newscited by 5
- 2024Deep Learning for Economistscited by 4
- 2025Reading the unreadable: Creating a dataset of 19th century English newspapers using image-to-text language modelscited by 3
- 2025DongbaMIE: A Multimodal Information Extraction Dataset for Evaluating Semantic Understanding of Dongba Pictogramscited by 3
- 2023Quantifying Character Similarity with Vision Transformerscited by 2
- 2023Linking Representations with Multimodal Contrastive Learningcited by 1
- 2025Layout-Aware OCR for Black Digital Archives with Unsupervised Evaluationcited by 1
- 2026Chronos: The AI Co-Historiancited by 0
- 2026Chronos: The AI Co-Historian4 Apr 2026
- 2025Layout-Aware OCR for Black Digital Archives with Unsupervised Evaluation16 Sep 2025
- 2025DongbaMIE: A Multimodal Information Extraction Dataset for Evaluating Semantic Understanding of Dongba Pictograms5 Mar 2025
- 2025Reading the unreadable: Creating a dataset of 19th century English newspapers using image-to-text language models18 Feb 2025
- 2024Deep Learning for Economists22 Jul 2024
- 2024Newswire: A Large-Scale Structured Database of a Century of Historical News13 Jun 2024
- 2023EfficientOCR: An Extensible, Open-Source Package for Efficiently Digitizing World Knowledge16 Oct 2023
- 2023American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspapers24 Aug 2023
- 2023Quantifying Character Similarity with Vision Transformers24 May 2023
- 2023Linking Representations with Multimodal Contrastive Learning7 Apr 2023