  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most existing methods for Historical Document Analysis focused on the visual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.
