OCR+大模型融合提取文档信息，提升准确率与语义理解
Digitization of Document and Information Extraction using OCR
- 结合OCR与大语言模型实现文本结构化
- 相比传统方法准确率显著提升，支持多类型文档
- 适合需要高精度信息抽取的场景，如合同、票据处理
从文档中准确获取信息是一项关键任务，尤其在处理扫描图像与原生数字格式混合的情况时。本文提出一种融合光学字符识别（OCR）与大语言模型（LLM）的综合框架，以生成具有上下文理解能力与置信度指标的结构化输出。扫描文件通过OCR引擎处理，数字文件则借助布局感知库解析。提取的原始文本由大语言模型分析，识别关键-值对并解决歧义。对比多种OCR工具，评估其在准确性、版面识别与处理速度方面的表现。该方法显著优于传统的规则与模板方法，在不同文档类别中展现出更强的灵活性与语义精确性。
原文摘要 · Abstract (English)
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
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