Dataset & Benchmark Results Experimental Benchmark & Evaluation 1. Archival Corpus Evaluated across 609 text segments spanning 45 digitized pages from 9 Swiss French-language historical periodicals (Canton of Vaud, 1733–1945): Periodical Era Issues Pages Segments Characteristics La Revue 1875–1945 4 5 139 19th/20th century serif, multi-column Feuille d'Avis 1762–1841 4 6 118 Antique typography, long 's' ligatures Mercure Suisse 1733–1748 3 5 92 18th-century early modern French Gazette de Lausanne 1804–1920 5 8 145 High density, complex layout Others (Tribune, etc.) 1880–1930 5 21 115 Mixed commercial & political press 2. Key Benchmark Results Error Rate Comparison (Tesseract OCR Base): Raw OCR Baseline: CER = 6.30\%, WER = 14.20\% All-LLM Baseline (GPT-4o / Gemini 1.5 Flash): CER = 2.92\% (Cost: 100% token budget) Selective Pre-Routing (Ours): CER = 3.12\% (Bypasses 40% of all segments!) Collaborative Safeguard (<5% Human Budget): CER = 2.49\% (14% relative reduction over All-LLM) Comparison with Routing Baselines: Random Routing: Weakest; frequently sends clean text and degrades it. OCR Confidence (Thresholding): Underperforms because Tesseract confidence scores poorly correlate with semantic error in archaic fonts. ConfBERT: Good detection but computationally heavy. Our Regression Router + Safeguard: Consistently establishes the Pareto frontier across all budget limits. Experimental Evaluation & Visual Benchmark Plots Historical Newspaper Segmentation Sample Figure 1: Representative historical archival newspaper segment illustrating layout artifacts and OCR degradation. CER Reduction Across Correction Strategies Figure 2: Character Error Rate (CER) reduction across baseline OCR, All-LLM, ConfBERT, and Collaborative Routing. OCR Engine Improvement Comparison Figure 3: Relative error reduction across historical OCR engines (Tesseract, Abbyy, and modern HTR). Strategy Performance Heatmap Figure 4: Comprehensive performance heatmap comparing LLM prompt strategies across historical periodicals.