Title: Information Extraction from Unstructured data using Augmented-AI and Computer Vision Show Chinese title

Title: 从非结构化数据中提取信息，使用增强型人工智能和计算机视觉

Abstract: Information extraction (IE) from unstructured documents remains a critical challenge in data processing pipelines. Traditional optical character recognition (OCR) methods and conventional parsing engines demonstrate limited effectiveness when processing large-scale document datasets. This paper presents a comprehensive framework for information extraction that combines Augmented Intelligence (A2I) with computer vision and natural language processing techniques. Our approach addresses the limitations of conventional methods by leveraging deep learning architectures for object detection, particularly for tabular data extraction, and integrating cloud-based services for scalable document processing. The proposed methodology demonstrates improved accuracy and efficiency in extracting structured information from diverse document formats including PDFs, images, and scanned documents. Experimental validation shows significant improvements over traditional OCR-based approaches, particularly in handling complex document layouts and multi-modal content extraction. Show Chinese abstract

Abstract: 信息抽取（IE）从非结构化文档中仍然是数据处理流程中的一个关键挑战。 传统的光学字符识别（OCR）方法和常规的解析引擎在处理大规模文档数据集时表现出有限的效果。 本文提出了一种综合的信息抽取框架，结合了增强智能（A2I）与计算机视觉和自然语言处理技术。 我们的方法通过利用深度学习架构进行目标检测，特别是针对表格数据抽取，并集成基于云的服务以实现可扩展的文档处理，从而解决了传统方法的局限性。 所提出的方法在从包括PDF、图像和扫描文档在内的各种文档格式中提取结构化信息方面表现出更高的准确性和效率。 实验验证显示，与传统的OCR方法相比有显著改进，特别是在处理复杂的文档布局和多模态内容抽取方面。