Papers for

government archives teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

System handles degraded historical archives with noisy ocr errors

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

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 layouts, and dense visual noise where OCR failure is the primary obstacle to information retrieval. We present an end-to-end framework that couples vision-based layout segmentation and multi-script OCR with an agentic retrieval-augmented generation (RAG) pipeline. Our approach specifically addresses how visual degradation and layout fragmentation propagate into text extraction errors, using an iterative agentic loop to navigate around OCR noise and layout ambiguities before generating answers. Evaluated on the 200,000-page Punjab Vidhan Sabha archive, our system demonstrates robust handling of historical document degradation, outperforming standard text-only baselines.

Mon 26 OctComputer Vision and Pattern RecognitionArtificial IntelligenceInformation Retrieval

The gist

Old government papers from India are hard to read because the pages are damaged and scanned with different languages and tricky layouts. The authors created a system that combines reading these mixed scripts with a smart, back-and-forth process to fix mistakes caused by bad text recognition. This approach helps the system better find information from the messy scanned documents. They tested it on a large collection of 200,000 pages and it worked better than usual methods that just use plain text.

Open → 2610.35240v1