论文标题
文档图像中的图形对象检测
Graphical Object Detection in Document Images
论文作者
论文摘要
图形元素：尤其是表格和数字包含文档中包含的最有价值信息的视觉摘要。因此，文档图像中此类图形对象的定位是了解此类图形对象或文档图像的内容的第一步。在本文中，我们提出了一个新颖的端到端可训练的基于深度学习的框架，以将图形对象定位在文档图像中，称为图形对象检测（上帝）。我们的框架是数据驱动的，不需要任何启发式方法或元数据才能在文档图像中找到图形对象。上帝探索了转移学习和域适应性的概念，以处理文档图像中图形对象检测任务的标记训练图像的稀缺性。对各种公共基准数据集进行了绩效分析：ICDAR-2013，ICDAR-POD2017，UNLV表明，与最新技术相比，我们的模型产生了令人鼓舞的结果。
Graphical elements: particularly tables and figures contain a visual summary of the most valuable information contained in a document. Therefore, localization of such graphical objects in the document images is the initial step to understand the content of such graphical objects or document images. In this paper, we present a novel end-to-end trainable deep learning based framework to localize graphical objects in the document images called as Graphical Object Detection (GOD). Our framework is data-driven and does not require any heuristics or meta-data to locate graphical objects in the document images. The GOD explores the concept of transfer learning and domain adaptation to handle scarcity of labeled training images for graphical object detection task in the document images. Performance analysis carried out on the various public benchmark data sets: ICDAR-2013, ICDAR-POD2017,and UNLV shows that our model yields promising results as compared to state-of-the-art techniques.
