论文标题
文档图像中用于数据摄入的机器学习框架
A Machine Learning Framework for Data Ingestion in Document Images
论文作者
论文摘要
纸质文件被广泛用作许多领域的不可替代的信息渠道，尤其是在金融行业中，促进了对可以将文档图像转换为结构化数据表示的系统的大量需求。在本文中，我们提出了一个机器学习框架，用于文档图像中的数据摄入，该框架处理用户上传的图像并以JSON格式返回细粒度数据。详细阐述了模型体系结构，设计策略，与现有解决方案的区别以及在开发过程中汲取的经验教训的详细信息。我们在州街的合成数据和现实世界数据上进行了丰富的实验。实验结果表明我们方法的有效性和效率。
Paper documents are widely used as an irreplaceable channel of information in many fields, especially in financial industry, fostering a great amount of demand for systems which can convert document images into structured data representations. In this paper, we present a machine learning framework for data ingestion in document images, which processes the images uploaded by users and return fine-grained data in JSON format. Details of model architectures, design strategies, distinctions with existing solutions and lessons learned during development are elaborated. We conduct abundant experiments on both synthetic and real-world data in State Street. The experimental results indicate the effectiveness and efficiency of our methods.
