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The segmentation of complex images into semantic regions has seen a growinginterest these last years with the advent of Deep Learning. Until recently,most existing methods for Historical Document Analysis focused on the visualappearance of documents, ignoring the rich information that textual content canoffer. However, the segmentation of complex documents into semantic regions issometimes impossible relying only on visual features and recent models embedboth visual and textual information. In this paper, we focus on the use of bothvisual and textual information for segmenting historical registers intostructured and meaningful units such as acts. An act is a text recordingcontaining valuable knowledge such as demographic information (baptism,marriage or death) or royal decisions (donation or pardon). We propose a simplepipeline to enrich document images with the position of text lines containingkey-phrases and show that running a standard image-based layout analysis systemon these images can lead to significant gains. Our experiments show that thedetection of acts increases from 38 % of mAP to 74 % when adding textualinformation, in real use-case conditions where text lines positions and contentare extracted with an automatic recognition system.

Including Keyword Position in Image-based Models for Act Segmentation of Historical Registers