Bootstrapping AI: Interdisciplinary Approaches to Assessing OCR Quality in English-Language Historical Documents
Samuel Backer, Louis Hyman · 2025
New LLM-based OCR and post-OCR correction methods promise to transform computational historical research, yet their efficacy remains contested.We compare multiple correction approaches, including methods for "bootstrapping" fine-tuning with LLM-generated data, and measure their effect on downstream tasks.Our results suggest that standard OCR metrics often underestimate performance gains for historical research, underscoring the need for discipline-driven evaluations that can better reflect the needs of computational humanists.