自动化发票数据提取：利用大语言模型和OCR
Automated Invoice Data Extraction: Using LLM and OCR
AI总结：
本文提出一个结合OCR、深度学习、LLMs和图分析的AI平台，以提高发票数据提取的质量和一致性。
摘要：
传统光学字符识别（OCR）系统在面对变票布局、手写文本和低质量扫描时面临挑战，这些通常是由强模板依赖性导致的，限制了其在不同文档结构和布局上的灵活性。较新的解决方案利用了先进的深度学习模型，如卷积神经网络（CNN）以及转换器，以及针对特定领域的模型，以在各种文档类型上实现更好的布局分析和准确性。大语言模型（LLMs）通过复杂的实体识别和语义理解，彻底革新了提取流程的核心，支持复杂的上下文关系映射，而无需直接编程指定。视觉命名实体识别（NER）能力使从发票图像中提取信息变得更加敏感和准确。现有的行业最佳实践利用混合架构，将OCR技术和LLM结合，以实现最大的可扩展性和最小的人工干预。本文介绍了一个综合的人工智能（AI）平台，结合OCR、深度学习、LLMs和图分析，以实现前所未有的提取质量和一致性。
英文摘要：
Conventional Optical Character Recognition (OCR) systems are challenged by variant invoice layouts, handwritten text, and low-quality scans, which are often caused by strong template dependencies that restrict their flexibility across different document structures and layouts. Newer solutions utilize advanced deep learning models such as Convolutional Neural Networks (CNN) as well as Transformers, and domain-specific models for better layout analysis and accuracy across various sections over varied document types. Large Language Models (LLMs) have revolutionized extraction pipelines at their core with sophisticated entity recognition and semantic comprehension to support complex contextual relationship mapping without direct programming specification. Visual Named Entity Recognition (NER) capabilities permit extraction from invoice images with greater contextual sensitivity and much higher accuracy rates than older approaches. Existing industry best practices utilize hybrid architectures that blend OCR technology and LLM for maximum scalability and minimal human intervention. This work introduces a holistic Artificial Intelligence (AI) platform combining OCR, deep learning, LLMs, and graph analytics to achieve unprecedented extraction quality and consistency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
