## 5 Discussion
Another challenging task is the mixture of several fonts or even scripts within a book, while even a mixture on a single page or even within a line is not uncommon, as we have seen during our Camerarius use case. We already showed Calamari's ability to reliably and accurately distinguish between different fonts even when their typefaces were very similar to each other [22]. Kraken [23] has also taken a few first steps in that direction by providing a generic model that can differentiate between Latin, Arabic, Greek, and Syirac script. After detecting the script, suitable OCR models can be applied to the (parts of) lines that match. In general, the applicability of fully automated methods does not only depend on the intended usage of the results, but also on the material at hand. We have shown that OCR4 all can achieve excellent results on 19 th Century Fraktur novels with a moderate layout and a suitable mixed OCR model available. As expected, the fully automatic processing of early printed books is a tricky task, and its applicability also highly depends on factors like layout and typography. The first experiments led to the following mostly qualitative observations:  
* The current setup can deal with relatively simple layouts consisting of a single or several well separated columns quite reliably. When several columns have to be identified, the user needs to specify the maximum number of columns occurring on a page. * Despite the lack of an explicit text/non-text segmentation, the combination of OCRopus 1 line segmentation and Calamari's recognition module is surprisingly robust against non-text elements like noise, artistic border elements, images, and swash capitals. Even if parts of these elements make it into a text line, they often do not deteriorate the text recognition result since Calamari will ignore them due to the lack of a confident recognition of available characters. * Marginalia that are located very close to the main text often cannot get separated correctly, leading to significant errors in the reading order. * Treating a page that comprises highly varying font sizes, for example a very prominent heading line and many running text lines whose characters are not even half as high, as a single text segment can lead to wrongly segmented lines. This happens because the line segmentation estimates the most likely height of a line on page level and then tries to find fitting lines. A preceding region segmentation prevents this problem from occurring. * The available mixed models work reasonably well on the majority of books, achieving an average CER of 7.7% on the corpus we used for our evaluations. However, since this is probably not good enough for most use cases, book specific training is necessary. Additionally, the CERs varied considerably, ranging from below 2% to over 25% on the new GT of the first iteration of each book, underlining the problem of mixed models we discussed before. To sum up, despite the open questions and challenges demonstrated above, OCR4 all can become a cornerstone when it comes to the high quality capturing of historical printings (a final summary of the obtained results is shown in Figure 12). By reducing the required technical know how to a minimum, it is now possible for humanities scholars to take the acquisition of their much desired and needed textual research data into their own hands.