## 5 Discussion
Our evaluations showed that a basic segmentation approach that simply ensures a proper text/non-text separation and a correct reading order can save around 40% of the time required for segmentation. While this represents a substantial speedup, the required manual effort is probably still too high for some areas of application. Yet, we have seen that the segmentation approaches currently available in OCR4 all cannot deal with more complex layouts in a (close to) fully automatic manner. Something that is clearly missing is a fully automatic way to deal with more complex layouts. While deep learning based approaches, mostly aiming to assign a layout able to each individual pixel, carry some promise, they are usually geared towards the training on and application to a single book (see for example [21]). However, when a basic segmentation (text/non-text separation and maybe a correct reading order) is considered sufficient, a mixed models approach for segmentation similar to the OCR one represents a very promising idea. Unfortunately, as of now, we are missing the required GT (page images and pixel labeling) to train these models. Admittedly, there is some fitting data more or less openly available, but usually, they show considerable shortcomings in terms of quality and accuracy, or only offer a manageable amount of data from a small number of books and/or a very specific time period. A comprehensive and versatile dataset of high quality segmentation GT is missing, especially for early printed books. The current semi-automatic and precise segmentation approach of OCR4 all allows producing a large amount of fitting GT with manageable effort. During the evaluations for this paper alone, more than 6000 pages of first-rate GT have been created. Naturally, the manual part of the segmentation represents a non-negligible extra effort and expense, but it also offers a double benefit, as it not only leads to higher OCR results for the (humanist) users, but it also allows the developers to utilize the new GT to train stronger mixed models for the segmentation task. Of course, the next step then would be to introduce an ITA for the segmentation task to OCR4 all, allowing the users to build from a reasonable result produced by a mixed model and then gear it towards the book at hand by correcting a few pages (hopefully) with minimal effort and then train a new model, which might then even be able to provide a more sophisticated semantic distinction of layout element if desired by the user and properly trained. When discussing the means necessary to increase the degree of automation, the need for a book-specific training also plays an important role. While our experiments have confirmed the effectiveness and efficiency of the ITA, it still represents a time consuming task. Despite our focus on projects intending to produce quotable text as their final result, we are aware of the fact that there are other use cases that, despite dealing with early printed books, aim for a more quantitative approach and therefore are willing to make sacrifices regarding text quality and semantic labeling. As we have seen above, despite our repertoire of mixed models trained on a wide variety of fonts geared towards the recognition of different font classes, a sufficient OCR quality, for example 95%+, cannot be guaranteed at all when working with early printed books. Of course, a wider variety of more specialized mixed models can help to improve the results. Just like with the segmentation task, the precise book-specific processing also creates a great deal of valuable OCR GT (over 25,000 lines during our experiments). Again, these data will be utilized for the continuous refinement of existing mixed models, basically resulting in a more broadly arranged ITA aiming to constantly reduce the required manual effort by optimizing the starting points for each book specific training.