## 4 Evaluations
### Interpretations  
Admittedly, the projection of the speedup achieved by the ITA was quite rough since the factor depended much on the pages the mixed model was applied to, which is also shown by the high fluctuations among the speedup factors. Moreover, in a real-world application scenario, there has to be some kind of training and testing during the correction phase in order to know when to stop, as the results from Table 5 showed that the number of lines needed to reach a certain CER varied considerably. Figure 10 depicts this problem and graphically explains the gain obtained by ITA. Determining the ideal training route is no trivial task and depends on several factors. To begin with, the user has to estimate how many lines are necessary to reach the desired CER. Due to the variety of the material, this is very challenging, even for experienced users, resulting in over- and under-estimations of the required amount. The smaller the chosen steps there are, the more accurate the convergence to the optimal value \(P\) (Figure 10) becomes. One (theoretical) approach is training a model each time a new line of GT is added (red curve, left); however, this is not sensible. The other end of the spectrum is represented by correcting the output of the mixed model until the presumably required number of GT lines is reached (green), which discards the gain of correcting lines with an improving CER (area ratio on the right). Consequently, the optimal or rather a sufficient real-world solution has to lie somewhere in between these two extremes. The available hardware plays an important role as it directly influences the training duration. For example, most training processes can be completed within a couple of minutes when using several GPUs, allowing the user to continue the transcription almost instantly. When no GPU support is available, a training can take several hours, requiring the user to perform different tasks. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Book Short**} & \multirow{3}{*}{**User Exp. **} & \multirow{3}{*}{**It. **} & \multicolumn{5}{c}{**New Data**} & \multicolumn{2}{c}{**Eval**} & \multicolumn{2}{c}{**All Data**} & \multicolumn{2}{c}{**Correction**} \\ \cline{3-13}  & & & \multirow{2}{*}{**\#P**} & & \multirow{2}{*}{**\#L**} & \multirow{2}{*}{ \begin{tabular}{c} \({}^{\text{L}}_{\text{Corr.}}\) \\ **(min)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} \({}^{\text{L}}_{\text{Corr.}}\) \\ **(s/L)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} \({}^{\text{L}}_{\text{Corr.}}\) \\ **(\%)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} \({}^{\text{L}}_{\text{Corr.}}\) \\ **(\%)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} \({}^{\text{L}}_{\text{Corr.}}\) \\ **(\%)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} \({}^{\text{L}}_{\text{Corr.}}\) \\ **(\%)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} \({}^{\text{L}}_{\text{Corr.}}\) \\ **\end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} \({}^{\text{L}}_{\text{Corr.}}\) \\ **\end{tabular}  } & \multirow{2}{*}{
\begin{tabular}{c} \({}^{\text{L}}_{\text{Corr. Despite the complexity of optimizing the ITA, its general benefits are clear, and the results confirm the expectations. This speedup is due to the fact that the average correction time per line clearly correlates with the quality (CER) of the underlying OCR result. The only exception can be seen in Iterations 1 and 2 of book C1566 where it took the user even a little longer to correct a line, despite starting from a somewhat better recognition result. Since a visual inspection of the concerned pages did not lead to new insights, it stands to reason that human factors like tiredness, form on the day, etc., play a non-negligible role.