# Toward Semi-Supervised Graphical Object Detection in Document Images
## 4 Experiments
### Results and Discussion
#### 4.4.3 Cross-Validation  
The datasets PubLayNet, IIIT-AR-13K, and DocBank are the most common datasets used for graphical objected detection, and the three datasets possess objects which are different from each other. To validate a model trained on one dataset with another dataset, there is a need to train models with the objects common to all the datasets. Table and Figure are the common classes among datasets, and they are different from each other; for instance, the IIIT-AR-13K dataset is based on business documents, the PubLayNet is created from publicly available docs, and DocBank is created in a way to ease layout analysis. The primary idea is to identify how similar the datasets are in terms of the graphical objects. The new datasets sub-PubLayNet, sub-IIIT-AR-13K and sub-DocBank that contain Table and Figure annotations are created, and multiple Soft Teacher models are trained with 5 and 10% of sub-PubLayNet, sub-IIIT-AR-13K, and sub-DocBank.  
The results of models trained on sub-PubLayNet and cross-validated on sub-IIIT-AR-13K and sub-DocBank are shown in Table 13. The Soft Teacher model trained on 10% of labeled trained data on Faster-RCNN with ResNet101 showed the highest mAP of 95.1%. The validation performed on other datasets proves that sub-DocBank is more similar to sub-PubLayNet than the sub-IIIT-AR-13K. Table 14 also proves that sub-DocBank is similar to sub-PubLayNet, and interestingly, the mAP on sub-PubLayNet is higher than that of sub-DocBank, even though the model is trained completely trained on sub-Docbank. From Table 15, it is shown the models trained on IIIT-AR-13K perform badly on the other two datasets. This is because the dataset has mostly business reports, unlike the other two datasets. Figure 12 depicts the qualitative evaluation of the table and figure on the sub-PubLayNet dataset by displaying samples of True Positives and False Positives.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Sub-PubLayNet**} & \multicolumn{3}{c}{**Sub-IIIT-AR-13K**} & \multicolumn{3}{c}{**Sub-DocBank**} \\ \hline
**Methods** & **Labeled Data** & **Table** & **Figure** & **mAP** & **Table** & **Figure** & **mAP** & **Table** & **Figure** & **mAP** \\ \hline \multirow{2}{*}{ST\_FRCNN\_R50} & 5\% & 95.9 & 92.1 & 94.0 & 59.7 & 23.0 & 41.4 & 72.7 & 49.0 & 60.8 \\ \cline{2-10}  & 10\% & 95.9 & 92.2 & 94.0 & 60.2 & 24.2 & 42.2 & 72.3 & 49.4 & 60.8 \\ \hline \multirow{2}{*}{ST\_FRCNN\_R101} & 5\% & 96.0 & 94.2 & 94.5 & 61.1 & 22.6 & 41.9 & 71.1 & 48.0 & 59.6 \\ \cline{2-10}  & 10\% & 96.1 & 92.9 & 95.1 & 57.3 & 16.6 & 37.0 & 66.2 & 48.0 & 57.1 \\ \hline \hline \end{tabular}
\end{table}
Table 13: The table represents the mAP@(0.5:0.95) of sub-PubLayNet, sub-IIIT-AR-13K and sub-DocBank validation datasets on a sub-PubLayNet trained model.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Sub-DocBank**} & \multicolumn{3}{c}{**Sub-PubLayNet**} & \multicolumn{3}{c}{**Sub-IIIT-AR-13K**} \\ \hline
**Methods** & **Labeled Data** & **Table** & **Figure** & **mAP** & **Table** & **Figure** & **mAP** & **Table** & **Figure** & **mAP** \\ \hline \multirow{2}{*}{ST\_FRCNN\_R50} & 5\% & 82.4 & 64.5 & 73.4 & 88.5 & 71.2 & 79.8 & 52.3 & 14.3 & 33.3 \\ \cline{2-10}  & 10\% & 82.5 & 65.3 & 73.9 & 89.0 & 72.0 & 80.5 & 60.6 & 28.0 & 40.3 \\ \hline \multirow{2}{*}{ST\_FRCNN\_R101} & 5\% & 80.7 & 65.3 & 73.0 & 84.9 & 65.1 & 75.0 & 55.5 & 18.2 & 36.9 \\ \cline{2-10}  & 10\% & 80.4 & 68.5 & 74.4 & 87.1 & 65.0 & 76.0 & 55.4 & 17.9 & 36.7 \\ \hline \hline \end{tabular}
\end{table}
Table 14: The table represents the mAP@(0.5:0.95) of sub-DocBank, sub-IIIT-AR-13K and sub-PubLayNet validation datasets on a sub-DocBank trained model.