在基于图像的历史寄存器行为分割模型中包含关键词位置
Including Keyword Position in Image-based Models for Act Segmentation of Historical Registers
摘要：
近年来，随着深度学习的出现，将复杂图像分割成语义区域的兴趣越来越大。直到最近，大多数现有的历史文档分析方法都集中在文档的视觉外观上，忽视了文本内容可以提供的丰富信息。然而，复杂的文档分割成语义区域有时是不可能的，仅仅依靠视觉特征和最近的模型嵌入视觉和文本信息。在本文中，我们专注于使用视觉和文本信息分割成结构化和有意义的单位，如行为的历史寄存器。法令是一种文本记录，包含有价值的知识，如人口统计信息（洗礼，婚姻或死亡）或皇家决定（捐赠或赦免）。我们提出了一个简单的管道来丰富文档图像的位置包含关键短语的文本行，并表明运行这些图像上的标准的基于图像的布局分析系统可以导致显着的收益。我们的实验表明，在使用自动识别系统提取文本行位置和内容的真实用例条件下，添加文本信息时，行为检测从mAP的38%增加到74%。
英文摘要：
The segmentation of complex images into semantic regions has seen a growing interest these last years with the advent of Deep Learning. Until recently, most existing methods for Historical Document Analysis focused on the visual appearance of documents, ignoring the rich information that textual content can offer. However, the segmentation of complex documents into semantic regions is sometimes impossible relying only on visual features and recent models embed both visual and textual information. In this paper, we focus on the use of both visual and textual information for segmenting historical registers into structured and meaningful units such as acts. An act is a text recording containing valuable knowledge such as demographic information (baptism, marriage or death) or royal decisions (donation or pardon). We propose a simple pipeline to enrich document images with the position of text lines containing key-phrases and show that running a standard image-based layout analysis system on these images can lead to significant gains. Our experiments show that the detection of acts increases from 38 % of mAP to 74 % when adding textual information, in real use-case conditions where text lines positions and content are extracted with an automatic recognition system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
