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.

翻译：传统的光学字符识别（OCR）系统在处理多样化的发票版式、手写文本以及低质量扫描件时面临挑战，这通常源于其强烈的模板依赖性，限制了系统在不同文档结构与版式间的灵活性。较新的解决方案采用先进的深度学习模型，如卷积神经网络（CNN）与Transformer，并结合领域专用模型，以提升对各类文档不同版面的分析能力与跨区域识别准确率。大型语言模型（LLM）通过其复杂的实体识别与语义理解能力，从根本上革新了信息提取流程，能够支持复杂的上下文关系映射，而无需直接编程定义。视觉命名实体识别（NER）功能使得从发票图像中提取信息具备更强的上下文感知能力，且准确率远高于传统方法。当前行业最佳实践采用混合架构，融合OCR技术与LLM，以实现最大可扩展性与最少人工干预。本研究提出一个整合OCR、深度学习、LLM及图分析技术的整体人工智能（AI）平台，旨在实现前所未有的提取质量与一致性。