# Presentation slides
### 📊 DocEng 2026 Conference Presentation Deck
Official 10-slide presentation delivered at ACM DocEng 2026 (28.08.2026).
[ 📥 Download PDF ](https://wiki.stergios.ch/attachments/22) [ 📥 Download PPTX ](https://wiki.stergios.ch/attachments/23)
### Slide 1: Title & Authors
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**Speaker Note:** Cost-Aware Human-LLM Collaboration for Post-OCR Corrections in Swiss Historical Newspapers (DocEng 2026, 28.08.2026). Presented by Stergios Konstantinidis, Hayman Lotfy, Prof. Michalis Vlachos.
### Slide 2: Motivation & Archival Scale
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**Speaker Note:** Building RAG systems for historical archives across 300+ years of newspapers, millions of pages, and 12 TB of raw high-resolution scan imagery.
### Slide 3: Why Document Cleaning is Not Straightforward
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**Speaker Note:** Post-OCR correction is essential for downstream retrieval, but traditional tools neglect historical linguistic shifts and archaic orthography.
### Slide 4: Heterogeneous Segment Utility
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**Speaker Note:** Different segments benefit very differently: Segment D gains +2.0% CER improvement with zero overcorrections, while Segment B suffers -3.0% CER degradation due to 10 overcorrections!
### Slide 5: Optimal Correction Prioritization
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**Speaker Note:** Theoretical premise: What if we knew in advance which archival documents would benefit most from LLM correction?
### Slide 6: The Real-World Dilemma
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**Speaker Note:** In reality, ground truth is unavailable. Naively correcting all documents produces unpredictable degradation and wastes massive token budgets.
### Slide 7: Our Contribution - Intelligent Routing Architecture
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**Speaker Note:** A machine-learned Router evaluating expected CER benefit: If LLM is sufficient -> route to LLM; if degradation is too severe -> route to human review (<5%); otherwise leave the segment untouched.
### Slide 8: Experimental Evaluation & Benchmark Curves
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[  ](https://wiki.stergios.ch/uploads/images/gallery/2026-09/doceng-slide-08.png "Click to inspect slide 8 in high resolution")
**Speaker Note:** Empirical results comparing baseline OCR, All-LLM, ConfBERT, and our Cost-Aware Routing framework across historical Swiss periodicals.
### Slide 9: Key Takeaways & Research Streams
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[  ](https://wiki.stergios.ch/uploads/images/gallery/2026-09/doceng-slide-09.png "Click to inspect slide 9 in high resolution")
**Speaker Note:** Key contributions: 40% reduction in correction effort, negligible setup cost, and an effective human guardrail identifying segments requiring expert archivist attention.
### Slide 10: Conclusion & Contact
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**Speaker Note:** Wrap-up, open questions, and research collaboration contact information at UNIL-DESI.