跨越 OCR 噪声：面向立法档案的鲁棒跨语言 RAG 框架
Reading Beyond OCR Noise: Robust Cross-Lingual RAG for Legislative Archives
AI 导读
研究者提出一套端到端框架，将视觉布局分割、多文字 OCR 与智能体式 RAG（检索增强生成）流程结合，应对印度历史议会档案中 OCR 失败这一主要障碍。该系统在 200,000 页的 Punjab Vidhan Sabha 档案上评测，通过迭代智能体循环绕过 OCR 噪声与版面歧义，在处理历史文档退化上优于纯文本基线。
正文 · 原文
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 Evaluated on the 200,000-page Punjab Vidhan Sabha archive, our system demonstrates robust handling of historical document degradation, outperforming standard text-only baselines.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2610.35240 [cs.CV] |
| (or arXiv:2610.35240v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35240 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: RadhaKrishna Deshpande [view email]
[v1]
Mon, 26 Oct 2026 11:24:16 UTC (6,934 KB)
来源：arXiv cs.AI · arxiv.org