## 2 Related Work
### Instance-Level Segmentation for Page Layout Analysis  
In addition to the previously presented methods, layout recognition can also be seen as an object detection task, where the goal is to identify semantically different regions in the image and distinguish separate instances. In contrast to labeling the image on a per-pixel level, object detection provides output bounding boxes or even masks for individual instances of detected regions in the image [19]. This is advantageous compared to pure segmentation, as, e.g., in multi-column layouts, single columns can be detected as individual instances. In contrast to pixel-level metrics such as the mIoU or accuracy for the page segmentation task, the metrics used for the object detection task, such as the mAP or the precision/recall measures on individual instances, take the instance segmentation into account. [PERSON] et al. [22] used YoloV5 [4] to detect twelve different page layout zones in historical documents. Afterwards, the results were injected into Kraken [23] to generate text lines and the OCR output. Likewise, [PERSON] et al. [24] applied a YoloV5 network to detect graphical elements, such as initials, decorations, printer's marks, or content illustrations, in different historical documents. The authors of [5] presented a method based on instance-level segmentation based on a modified Mask-RCNN architecture to detect different layout elements in popular datasets such as PubLayNet, containing more modern document images, or the HJ dataset, containing historical Japanese document images. A different approach was taken by [25], who presented a Transformer-based model for instance-level segmentation. Their model consisted of a feature extracting feature pyramid network (FPN), which then was fed into a Transformer. Then, the features with global context as the output from the Transformer were combined with the local FPN features to form the final segmentation and instance segmentation outputs. An approach utilizing different input representations for modern documents was presented by [PERSON] et al. [26]. They used the page's input in conjunction with CharGrid and SentGrid representations of the document to build a layout recognition model using a separate visual and semantic part consisting of convolutional neural networks, which were then combined in a multiscale adaptive aggregation module. The outputs of this module were fed into a graph neural network, which postprocessed the resulting region proposals to produce the final instance-level segmentation of the document. A rule-based approach for instance-level page segmentation was presented by [27]. In this system, a set of handcrafted rules is applied to classify text lines and text regions into different semantic classes such as title, first line, header, text, or other on a custom French newspaper dataset. In addition, the authors utilized the RIPPER method to automatically induce the rules for the classification from a training dataset. However, it has to be noted that this method requires text regions and the corresponding OCR as the input. This is also a key difference from our proposed method, as our rule-based system does not rely on annotation regions and OCR, but instead, only on the output of a baseline detector with additional postprocessing. The comparison of different layout segmentation approaches is difficult, as individual methods are usually evaluated on different datasets. Although for modern document layouts, datasets such as PubLayNet [2] exist and provide a common ground for comparison, there are only a few commonly used datasets for historical documents, such as the IlluHisDoc dataset [11] for illustration detection or the DivaHisDB dataset [28] for the segmentation of different text categories. In addition, there exist multiple metrics for layout recognition, as different outputs require different metrics.