## 2 Related Work  
Layout analysis is an important first step in the digitization and processing of document images. In general, the layout analysis task can be described as detecting regions of different semantic types in the document image [1]. One important aspect to consider for this task is the detail level at which the page layout should be analyzed. While there are many approaches focusing only on text, non-text, and background segmentation, recent approaches tend to classify elements on the page into increasingly more refined categories. As an example, for the common PubLayNet dataset [2], the goal is to detect the main text regions, titles, figures, tables, and lists. Recently, due to the rise of increasingly capable NLP models, the tasks in layout analysis can also involve the classification of the semantic roles of the text inside the various regions, as described in [3]. Here, the authors compared textual to hybrid approaches such as LayoutLM and visual approaches such as YoLoV5 [4], to distinguish the text roles such as primary text, commentaries, translations, and critical apparatuses in datasets from the 19 th Century. As our paper mainly focused on the visual aspect of layout recognition, we present primarily visual approaches for this task in the following. This is especially of interest regarding the fact that many more detailed approaches often require a prior step, in which text elements have already been extracted and transcribed. Although the task of layout recognition can be approached using a single step using modern neural networks, as for example described in [5], many related works still focus on improving individual aspects of the pipeline, such as text or illustration detection in historical documents or table recognition in modern documents. Three main types of approaches to this problem can be identified, which differ in their roles in complete document processing pipelines, as well as their produced outputs. The first category comprises pixel-level segmentation approaches. Such methods take a document image as the input and assign a class label to each individual pixel in the image. The labels can range from just a text/non-text distinction to individual class labels for different types of text elements such as headings, footers, titles, the main text, etc. Clearly, these methods are just a first step in the layout analysis as, for the subsequent OCR, individual regions or text lines have to be extracted from the output in a postprocessing step, as such annotations are a requirement for the subsequent OCR and further document processing [6]. This can be challenging if the output of the segmentation is not perfectly accurate, which could lead to the reduced performance of the text recognition system. The second category comprises text detection approaches, which focus on the localization of textual elements, such as printed lines, or handwritten notes, or even handwritten artifacts on printed pages. It is important to note that these approaches differ from the segmentation category, as their goal is not only to detect the location of text elements, but also to determine the individual instances of text objects, such as individual lines in the document image. These methods have the advantage of the detected textual elements being able to be directly fed into the OCR. In addition, the document's visual layout can easily be reconstructed from the detection, as the locations of the text elements are also detected in this process. The last category of methods can be described as generic instance-level segmentation approaches. Similar to the task in [1], the goal of these methods is to extract the region instances of layout elements such as text lines, text regions, title regions, dropped capital regions, as well as non-text instances, such as illustrations, figures, or separators. This category can be seen as a combination of the two prior categories, as these approaches deliver classified regions, which can be directly fed into OCR engines for further processing.