# Toward Semi-Supervised Graphical Object Detection in Document Images
## 5 Conclusions and Future Work  
This paper presents the semi-supervised framework for graphical page object detection by employing a multi-stage semi-supervised technique known as Soft Teacher for the detection of graphical objects. In addition to operating on minimal data, this approach combines the complex process of pseudo labeling into a single pipeline. As the framework generates pseudo labels simultaneously with detection training, the flywheel effect occurs, which means one model constantly refines the pseudo boxes generated by the other model during the training process. This framework refines the classification and regression pseudo boxes using two different techniques, Soft Teacher and Box Jittering. These two processes work independently to render accurate classification and bounding box predictions.  
This approach outperforms the supervised models in the labeling ratios (1%, 5%, and 10%) of PubLayNet and IIIT-AR-13K training data. Additionally, the Soft Teacher models trained on 10% PubLayNet labeled data performed similarly to the existing supervised baseline, whereas the Soft Teacher model on IIIT-AR-13K outperformed the YOLO F model. Finally, new datasets are created to perform inter-dataset cross-validation and prove that PubLayNet and DocBank datasets are more related compared to IIIT-AR-13K.  
In future work, we plan to use powerful two-stage detectors such as Cascade-RCNN for better detection results compared to the current Faster-RCNN. Furthermore, we will examine the effect of the percentage of labeled data on the final performance and try to design robust models relying on minimal annotated data. Finally, the aforementioned  
Figure 12: The results of out model equipped with Faster R-CNN and ResNet101 and trained on 5% of sub-PubLayNet labeled data. Blue represents True Positives and Red represents False Positives. In this figure, (**left**) represents a sub-DocBank sample with a True Positive and False Positives, (**right**) shows True Positives and False Positives on a sub-IIIT-AR-13K sample.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Sub-IIIT-AR-13K**} & \multicolumn{4}{c}{**Sub-PubLayNet**} & \multicolumn{4}{c}{**Sub-DocBank**} \\ \hline
**Methods** & **Labeled Data** & **Table** & **Figure** & **mAP** & **Table** & **Figure** & **mAP** & **Table** & **Figure** & **mAP** \\ \hline \multirow{2}{*}{ST\_FRCNN\_R50} & 5\% & 86.3 & 51.5 & 68.9 & 73.1 & 25.8 & 49.4 & 33.6 & 20.5 & 27.0 \\ \cline{2-11}  & 10\% & 88.0 & 54.8 & 71.4 & 75.7 & 31.0 & 53.3 & 54.3 & 29.0 & 41.7 \\ \hline \multirow{2}{*}{ST\_FRCNN\_R101} & 5\% & 87.3 & 45.8 & 66.5 & 75.4 & 31.3 & 53.4 & 60.4 & 31.3 & 45.8 \\ \cline{2-11}  & 10\% & 85.5 & 48.3 & 67.4 & 78.3 & 21.2 & 49.7 & 56.0 & 23.0 & 39.5 \\ \hline \hline \end{tabular}
\end{table}
Table 15: The table represents the mAP@(0.5:0.95) of sub-IIIT-AR-13K, sub-PubLayNet and sub-DocBank validation datasets on a sub-IIIT-AR-13K trained model.  
results show that the proposed framework with Faster R-CNN performs similarly to Faster R-CNN; nevertheless, we must show that this statement holds for multiple backbones. Finally, to establish a baseline for semi-supervised graphical page object detection, we want to assess various semi-supervised frameworks, including consistency and pseudo labeling.  
Writing--original draft preparation, G.K., K.A.H., M.Z.A.; writing--review and editing, K.A.H., M.Z.A., M.L.; supervision and project administration, A.P., D.S. All authors have read and agreed to the submitted version of the manuscript.  
The work leading to this publication has been partially funded by the European project INFINITY under Grant Agreement ID 883293.  
Not applicable.  
The authors declare no conflict of interest.