mlsanityData to 7 Oct 2026

Efficient OCR for Building a Diverse Digital History

arXiv 2304.02737 ↗First posted 5 Apr 2023cs.CV cs.DL econ.GNcited by 10

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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.

References · 38

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26 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 2; 2025: 3; 2026: 1.

10 papers.