## 4 Evaluations
### Precise Segmentation and Trained OCR of Early Printed Books
#### 4.3.2 Overall Time Expenditure and OCR Accuracy  
In this first experiment, we evaluated the two main criteria for a workflow with considerable human interaction: the time that had to be invested both for obtaining a sufficient result regarding segmentation and OCR, as well as the achieved OCR accuracy. These criteria were heavily influenced by several factors, which have to be taken into consideration. First, the experience of the user: An experienced user can be expected to be more efficient, both during the segmentation and the GT production phase. In our experiments, we differentiated between two groups of users: On the one hand, there were several first time users with no experience with OCR4 all and no or next to no experience with other OCR related tools and processes. On the other hand, we had users with a solid general OCR background and an extensive history of using OCR4 all, LAREX, and various transcription tools. In the following, we assign the labels 1 and 2 to the users of the respective groups, with a higher number indicating a more experienced user. To distinguish the individual users from each group, we assign additional labels (A, B, etc.). With the exception of one experienced user (digital humanities), all users were classical humanities scholars. Before starting to work on their respective books on their own, all participants were introduced to the tool by one of the experienced users. Second, challenges due to the book: Different books can vary considerably, mainly regarding the number of pages, the complexity of the layout, but also the print or scan quality and overall state of preservation. Since the books utilized during our experiments did not show large discrepancies concerning the latter criteria, we just provide the number of pages and distinct semantic layout classes. Table 5 sums up the results. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Book Short**} & \multirow{3}{*}{**User Exp. **} & \multicolumn{4}{c}{**Segmentation**} & \multicolumn{4}{c}{**OCR**} & \multicolumn{2}{c}{**Key Figures**} \\ \cline{3-11}  & & \multirow{2}{*}{**\#P**} & \multirow{2}{*}{**\#R**} & \multicolumn{2}{c}{\(\mathbf{f_{Seg. }}\)} & \multicolumn{2}{c}{\(\mathbf{f_{Seg./P}}\)} & \multirow{2}{*}{**\#L**} & \multicolumn{2}{c}{**B**} & \multicolumn{2}{c}{\(\mathbf{t_{Corr. }}\)} & \multicolumn{2}  
Results  
For the less experienced users, an average segmentation expense of slightly more than one minute per page was recorded with considerable variations among different users. Fortunately, more experienced users can speed up the process considerably, resulting in about 36 seconds per page on average, again with considerable variations depending on the layout complexity of the book. Regarding the time expenditure required for correcting OCR results for GT production, the vast majority of users invested less than ten seconds per line on average. Both user groups achieved almost identical CERs (0.47% and 0.49%) by utilizing a very similar amount of GT (988 and 927 lines). These results enabled us to compare the time expenditure of the users on a more general level by taking the achieved OCR quality out of the equation. Calculating the time required to process a book, both segmenting it and creating enough GT to obtain an average CER of below 0.5%, resulted in just 0.7 min per page for the experienced users. Compared to the 2.3 min achieved by the inexperienced users, this represents a speedup of more than a factor of three. Interpretation  
The times accounted for segmentation clearly showed that performing a precise and fine grained semantic segmentation of early printed books, even when using a comfortable and versatile tool like LAREX, can still amount to several hours of work for a single book; plus the time to generate GT (either from scratch or by correcting an OCR result) and to train an OCR model.