The current digital environment is characterized by the widespread presence of data, particularly unstructured data, which poses many issues in sectors including finance, healthcare, and education. Conventional techniques for data extraction encounter difficulties in dealing with the inherent variety and complexity of unstructured data, hence requiring the adoption of more efficient methodologies. This research investigates the utilization of YOLOv5, a cutting-edge computer vision model, for the purpose of rapidly identifying document layouts and extracting unstructured data. The present study establishes a conceptual framework for delineating the notion of "objects" as they pertain to documents, incorporating various elements such as paragraphs, tables, photos, and other constituent parts. The main objective is to create an autonomous system that can effectively recognize document layouts and extract unstructured data, hence improving the effectiveness of data extraction. In the conducted examination, the YOLOv5 model exhibits notable effectiveness in the task of document layout identification, attaining a high accuracy rate along with a precision value of 0.91, a recall value of 0.971, an F1-score of 0.939, and an area under the receiver operating characteristic curve (AUC-ROC) of 0.975. The remarkable performance of this system optimizes the process of extracting textual and tabular data from document images. Its prospective applications are not limited to document analysis but can encompass unstructured data from diverse sources, such as audio data. This study lays the foundation for future investigations into the wider applicability of YOLOv5 in managing various types of unstructured data, offering potential for novel applications across multiple domains.

翻译：当前数字环境以数据的广泛存在为特征，尤其非结构化数据在金融、医疗和教育等领域引发诸多问题。传统数据提取技术在应对非结构化数据固有的多样性和复杂性时面临挑战，亟需采用更高效的方法。本研究探讨了利用前沿计算机视觉模型YOLOv5快速识别文档布局并提取非结构化数据的应用。本文构建了一个概念框架，用于界定文档中“对象”的概念，涵盖段落、表格、图片及其他构成要素。主要目标是创建一个自主系统，能够有效识别文档布局并提取非结构化数据，从而提升数据提取效率。在实验分析中，YOLOv5模型在文档布局识别任务中表现出显著效能，实现了高准确率，精确率达0.91，召回率达0.971，F1分数为0.939，以及受试者工作特征曲线下面积（AUC-ROC）为0.975。该系统的卓越性能优化了从文档图像中提取文本和表格数据的过程。其潜在应用不仅限于文档分析，还可扩展至来自多种来源的非结构化数据，例如音频数据。本研究为未来探索YOLOv5在管理各类非结构化数据方面的更广泛应用奠定了基础，为跨领域的创新应用提供了可能。