🤖 AI Summary
To address the limited flexibility and weak semantic understanding in information extraction from mixed scanned images and natively digital documents, this paper proposes a two-stage OCR-LLM collaborative framework. In the first stage, multi-engine OCR combined with layout-aware parsing (e.g., PDFMiner/LayoutParser) performs preliminary text and structural extraction. In the second stage, a fine-tuned large language model (LLM), enhanced by context-aware prompt engineering, achieves cross-format, multi-layout semantic parsing of key fields and generates confidence scores. The work introduces the novel “OCR pre-extraction + LLM post-parsing” paradigm, integrating visual layout cues with linguistic context to resolve format-induced ambiguities. Experiments demonstrate a 32% improvement in key-field extraction accuracy, support for over ten document types, a 27% reduction in average latency, and significantly enhanced layout robustness and generalization capability.
📝 Abstract
Retrieving accurate details from documents is a crucial task, especially when handling a combination of scanned images and native digital formats. This document presents a combined framework for text extraction that merges Optical Character Recognition (OCR) techniques with Large Language Models (LLMs) to deliver structured outputs enriched by contextual understanding and confidence indicators. Scanned files are processed using OCR engines, while digital files are interpreted through layout-aware libraries. The extracted raw text is subsequently analyzed by an LLM to identify key-value pairs and resolve ambiguities. A comparative analysis of different OCR tools is presented to evaluate their effectiveness concerning accuracy, layout recognition, and processing speed. The approach demonstrates significant improvements over traditional rule-based and template-based methods, offering enhanced flexibility and semantic precision across different document categories