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New RAG framework tackles OCR noise in historical archives

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel framework to improve information retrieval from historical legislative archives, specifically addressing challenges posed by degraded documents and OCR errors. Their system integrates vision-based layout segmentation and multi-script OCR with an agentic retrieval-augmented generation (RAG) pipeline. This iterative approach navigates visual degradation and layout ambiguities to enhance text extraction, demonstrating superior performance on the Punjab Vidhan Sabha archive compared to standard text-only methods. AI

IMPACT This research could improve access to historical documents and inform future RAG system development for challenging datasets.

RANK_REASON The cluster contains an academic paper detailing a new technical approach to information retrieval.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New RAG framework tackles OCR noise in historical archives

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The cluster contains an academic paper detailing a new technical approach to information retrieval.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Radha Krishna Deshpande, Ajoy Mondal, Akash Manna, C V Jawahar ·

    Reading Beyond OCR Noise: Robust Cross-Lingual RAG for Legislative Archives

    arXiv:2610.35240v1 Announce Type: cross Abstract: India's historical public archives present severe computer vision and document image analysis challenges: decades of parliamentary records survive only as degraded, multi-column scanned pages featuring mixed scripts, complex layou…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · C V Jawahar ·

    Reading Beyond OCR Noise: Robust Cross-Lingual RAG for Legislative Archives

    India's historical public archives present severe computer vision and document image analysis challenges: decades of parliamentary records survive only as degraded, multi-column scanned pages featuring mixed scripts, complex layouts, and dense visual noise where OCR failure is th…

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