Overview & Executive Summary

1. Research Motivation & Core Problem

Historical document archives, such as digitized centuries-old Swiss newspapers, suffer from substantial OCR degradation caused by aged paper, bleed-through, complex multi-column typography, and antique fonts.

While modern Large Language Models (LLMs) excel at OCR correction, naive all-to-LLM pipelines suffer from three severe pitfalls:

1. Financial Cost: Processing millions of archive pages with commercial LLM APIs is economically non-viable.
2. Degradation of Clean Text: LLMs frequently "hallucinate" or modernize archaic French spellings on segments that were already correctly transcribed by the OCR engine.
3. Severe OCR Failures: On heavily damaged regions, LLMs fabricate plausible-sounding text instead of preserving historical fidelity.

This paper formulates OCR post-correction as an optimal budget allocation and routing problem uniting human domain experts and LLMs.

2. Key Contributions

• Three-Tier Human-LLM Collaboration: Explicit routing of each segment to either *(1) No Correction*, *(2) Automated LLM Correction*, or *(3) Human Review*.
• Regression-Guided Prioritization: Learned Lasso/Ridge router predicting expected Character Error Rate (CER) reduction \hat{Δ}_i before sending tokens to an LLM.
• Post-LLM Safeguard Layer: Classification mechanism detecting when LLM correction introduced regression or hallucination, diverting compromised outputs to human reviewers.
• Superior Budget Efficiency: Achieves 14% relative CER reduction over the All-LLM baseline with <5% human review budget.