## 8 Analysis of Commonly Occurring Errors and Discussion  
In this section, we analyze commonly occurring errors in the predictions. The number of occurrences of these errors were counted for the CamStd, CamMarg, GW5051, and GW5056 datasets manually, using the visualizations provided by our evaluation script. The results of the analysis are presented in Table 5.  
Our evaluation for the layout recognition shows, in Table 4, that overall, we achieved good results on the tested datasets. Especially, it has to be noted that, for all datasets, all main text line instances were located by our baseline detection method for the analyzed datasets. In general, the main text line detection errors were the result of missing parts due to erroneously detected marginalia cut locations, falsely detected dropped capitals, missing letters at the beginning or endings of the line, or incorrect column separation. Some elements, such as marginalia, small headers, or footers, can be observed to be missing entirely from the baseline prediction and, therefore, the resulting segmentation, as explained in detail in the following. In some cases, additional baselines were detected, such as parts of images, background noise, or degradations, which resulted in false positives in the different text classes, depending on their location on the page and their classification by the rulebook. On the CamStd dataset with no marginalia and a standard layout, almost all elements were successfully detected and no additional lines were present in the prediction, except for two text lines, which were mistaken for headers. On the more complex CamMarg dataset, the results showed that the main page layout was, in all cases, successfully detected as single column as no column classes were predicted. Only five main text lines were not detected, which was the result of inaccurate marginalia cuts or dropped capitals predicted in the text region. The line endings were also correct in more than 99% of the cases, except for the marginalia class, where sometimes, missing asterisk marks in the front led to the line endings being incorrect. More than 97% of the marginalia regions were detected correctly. As the marginalia are often printed in small letters, background noise from the binarization can lead to an inaccurate polygonization, leading to small errors at the beginning and endings of the marginalia. As the difference between the line ending accuracy with the 1% threshold suggests, these errors were small, often less than the width of a single letter. The error analysis in Table 5 showed that only one of the detected marginalia was incorrectly cut. The main errors concerning marginalia in this dataset were that 11 marginalia were not predicted at all by our baseline detector due a too small size. For the header category, three segmentation errors occurred, when page numbers were not detected by the baseline detection, and therefore, the header polygon was too small, missing a significant part. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Example Image** & **Error Type** & **CamStd** & **CamMarg** & **GW5051 (m)** & **GW5056 (m)** \\ \hline \hline \multirow{2}{*}{ \begin{tabular}{} \end{tabular}  } & Marginalia/column not separated & 0 & 0 & 0 & 8 \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & \\  & & & & & \\  & & & & & \\  & & & & \\  & & & & & \\  & & & & \\  & & & & & \\  & & & & \\  & & & & & \\  & & & & \\  & & & &Furthermore, the dropped capital detection predicted all existing dropped capitals in the CamStd and CamMarg datasets correctly.