## 5 Discussion  
Before discussing the results and their meaning in detail, we sum up the main findings of our experiments:  
* The experiments with 19 th Century Fraktur novels showed that a fully automated application of OCR4 all is not only possible, but can be highly precise on material with moderate complex layouts and if a suitable OCR model is available (average CER of 0.85% compared to ABBYY's 5.3%). * When dealing with challenging early printed books, inexperienced users had to invest 2.3 min per page on average to perform a precise segmentation and to reach a CER of below 0.5%, which highlights the effectiveness of the proposed approach. Experienced users can perform much more efficiently, reaching a speedup factor of more than 3 (0.7 min per page on average). * The ITA yielded significant speedups (factor 1.9) compared to the naive correction of the output of the mixed model. Figure 11: Representative example image of two books showing the difference between the basic (from left to right: 1,3) and exact (2,4) segmentation approach for books C1541 (1,2) and N1506 (3,4). * A basic segmentation approach that only ensures a sufficient text/non-text separation and a correct reading order reduces the manual effort required for segmentation considerably by a factor of more than 2.5. The obtained results showed that OCR4 all fulfilled its purpose by enabling non-technical users to capture even the earliest printed books completely by their own and with great quality, despite the challenges provided by complex layout and irregular typography. Due to our strict demands regarding the semantic classification of layout elements and our goal of high OCR quality, a considerable amount of manual work was required and accepted. While the experiments showed that even non-technical users without any background or previous experience in OCR were comfortably able to successfully work with OCR4 all, the results also showed that there is a learning curve and that experience is key. This holds true for both the segmentation, as well as the OCR, with experienced users being almost twice as fast when it comes to segmenting a page or transcribing a text line compared to inexperienced users on average. However, the quality of the result was not influenced by the experience of the user, with both groups achieving an excellent average CER of slightly below 0.5%. Regarding the two main steps that require manual intervention, segmentation, and OCR, the first one seems to show more room for improvement since the OCR of historical printings has made great progress over the last few years, which could also be observed during our experiments. Calamari's training and recognition capabilities combined with the easy-to-use ITA provided by OCR4 all allow the users to utilize state-of-the-art deep learning software and accuracy improving techniques like pretraining, voting, and data augmentation without ever being forced to acquire a deeper understanding of the technical concepts behind them. As shown by the evaluations, CERs below 1% or even 0.5% should almost be considered the norm after a thorough book-specific training was performed. The segmentation using LAREX proved to be intuitive and highly accurate. While the usage of OCR4 all reduced the manual effort necessary to transcribe early printed books tremendously, especially compared to the fully manual approach, which often required several weeks of full-time transcribing to process a single book, we still think that there is room for improvement. While a fully automated approach where it is not necessary to look at every single page seems to be currently out of reach, not only due to the complexity of the layouts, but also due to the very high demands of the users regarding the quality of the segmentation and degree of semantic distinction, we think that the efficiency of this part of the workflow can be further increased.