SocOCRbench – An OCR benchmark for social science documents
SocOCRbench is a new benchmark designed to evaluate OCR systems on social science documents, addressing the unique challenges of historical texts, tables, and non-standard layouts often found in social science research materials.
Background
- The author, Noah Dasanaike, introduces **SocOCRbench**, a new benchmark (standardized test) for measuring how well Optical Character Recognition (OCR) systems perform on the kinds of documents social scientists actually use: historical newspapers, parliamentary records, government reports, scanned books, and archival material from the 18th–20th centuries.
- Existing OCR benchmarks focus on clean modern documents (books, receipts, signs). Social science research often depends on messy historical scans—faded ink, decorative fonts, smudges, columns, and archaic typefaces—which standard OCR systems handle poorly.
- The benchmark contains manually transcribed ground-truth pages from sources like the British Newspaper Archive, US Census reports, and the French National Library. It tests both character-level and word-level accuracy.
- This matters because errors in digitized historical texts can propagate into large-scale quantitative social science (text-as-data, natural language processing of historical corpora), potentially biasing research findings. SocOCRbench gives researchers a way to choose an OCR tool suited to their source material and to quantify downstream error risks.