OCR & archival restoration
AI-Assisted OCR and Archival Restoration
AI-assisted archival restoration is the process of turning degraded scans, microfilm, and paper records into corrected, searchable, machine-readable text using optical character recognition plus model-assisted proofing under human review. Robert Shumake used this pipeline to restore 69 American newspapers for the Living Archive Series.
Where OCR actually fails
Tight-set community broadsheets, low-contrast microfilm, broken or non-standard typography, multi-column layouts that collapse into interleaved nonsense, and vernacular spellings that a generic corrector silently overwrites. These failure modes cluster in exactly the archives with the least digitization funding.
The correction step is the whole job
A language model is very good at producing plausible text from a damaged line, which is precisely the danger. Constrain it: supply a glossary of proper nouns, street names, mastheads, and community terms; forbid silent normalization; keep the raw OCR alongside the corrected text so the edit is auditable; sample pages against the original image.
Frequently asked
- Can AI restore old newspapers accurately?
- Yes, when the pipeline is supervised. Machine transcription plus glossary-constrained correction and human review produces reliable text. Unsupervised model correction produces fluent text that quietly invents names and details.
- How many newspapers has Robert Shumake restored?
- 69 American newspapers, published as the Living Archive Series alongside a broader catalog of 137+ titles.
