## 8 Analysis of Commonly Occurring Errors and Discussion
In the CamMarg dataset, three additional dropped capitals regions were incorrectly predicted in text lines, which led to errors at the beginning and ending of those text lines. In the future, these false positives could be eliminated by applying rules for the postprocessing of the dropped capitals, e.g., by utilizing information from a subsequent OCR to detect that characters from the words are missing. Furthermore, the evaluation on the CamStd, CamMarg, and CamManual datasets suggested that, for single-column layouts, our detection heuristics worked. For the GW5051 dataset, it can be seen that the columns were, in most cases, successfully detected. In all cases, it was successfully determined that the columns should be separated. Only in 34 instances were two columns not separated exactly and part of the text in the column was cut. In addition, using the special rule for cutting the top line into the header and headline (text), only 4 out of 67 folio numbers were not correctly segmented. This could in particular be further improved by an OCR, as the folio numbers consist of Latin numerals and could, therefore, be distinguished from the text in the headline. Furthermore, as our header rule only applies to the top line, it can be seen from the error analysis that, if noise is detected as text above the top line (in one case in this dataset), the header cannot be detected and, therefore, is not be separated from the headline. In addition, the dataset contains signature marks on the same line as the last text line, which we did not expect and is, thus, not covered by the rulebook. This led to one footer element not being detected correctly, as it was not separated from the main text in the same column. Furthermore, some footer elements were not detected, as they were very small and, thus, not recognized as text by our baseline detector. In addition, two text elements, which were printed similarly to footers, were falsely detected as footers in this dataset, which we believe could only be detected by using the transcription provided by the OCR. The evaluation of the GW5056 dataset showed that, although the main text lines had ragged left and right edges, the main text was still detected successfully for the most part as 99.6% of the text lines were detected, out of which 99.7% had accurate line endings. Five of the detected text lines were incorrectly classified as headers, because no header was present on these pages and our rules were built on the assumption that a header is present on every page. As the error analysis showed, the marginalia were detected in more than 98% cases with only one instance, where the marginalia were detected as header elements, as the header element was not detected on that page. The error analysis showed that there were only 8 instances where marginalia were not separated from the main text and 18 instances where the separation was inaccurate. Although many footer elements in the dataset consisted of spaced individual letters, it can be seen that these were accurately detected, but only 26 out of 28 were predicted to be of the footer class. Additionally, it can be seen that 2 and 6 text lines were erroneously detected in image regions in the GW5051 and GW5056 datasets, respectively. In the future, this could be improved with a postprocessing step by also detecting the image regions, e.g., using an object detector such as a Mask R-CNN, similar to our dropped capital detection. The evaluation of the additional CamManual dataset showed that, even though handwritten annotations and artifacts were present, the main text was detected more than 99% of the time. For the header category, similar errors as in the CamMarg dataset can be observed, as sometimes, small page numbers were not detected. In addition, many marginalia regions in this dataset were very short, often consisting of a single astrological symbol. Only 91% of the marginalia regions were detected. In addition, handwritten artifacts were often detected as marginalia or interfered with the marginalia separation.