# Continual Learning for Table Detection in Document Images
## 5 Conclusions and Future Work  
Continual-learning-based methods have shown promising results in computer vision [16; 47] and natural language processing [68]. In this paper, we present an incremental  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **TB[mAP]** & **PN[mAP]** & **PT[mAP]** & **FN[mAP]** \\ \hline CDeC-Net [31] & 96.5 & 97.8 & - & - \\ CasTabDetectoRS [14] & 95.3 & - & - & - \\ DETR [66] & - & - & 96.6 & - \\ \hline Faster R-CNN+ResNet (ours) * & 89.6 & 93.7 & 97.4 & 90 \\ Sparse R-CNN+PVT (ours) * & 90.7 & 92.5 & 98.2 & 93.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The mAP results of SOTA methods and our continual methods. * indicates that the results are not directly comparable. Acronyms TB, PN, PT, and FN denote TableBank, PubLayNet, PubTables-1M, and FinTabNet, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **Train-Set/Test-Set** & **Faster R-CNN+ResNet** & **Sparse R-CNN+PVT** \\ \hline FT & \(PT\to PN\to TB\to FN/PT\) & 96.1 & 97.4 \\ ER & \(PT\to PN^{R_{1}}\to TB^{R_{1,2}}\to FN^{R_{1,2}}/PT\) & 97.7(+1.6) & 98.5(+1.1) \\ \hline FT & \(PT\to PN\to TB\to FN/PN\) & 91.2 & 93.4 \\ ER & \(PT\to PN^{R_{1}}\to TB^{R_{1,2}}\to FN^{R_{1,2}}/PN\) & 94.2(+3) & 94.4(+1) \\ \hline FT & \(PT\to PN\to TB\to FN/TB\) & 76.5 & 79.8 \\ ER & \(PT\to PN^{R_{1}}\to TB^{R_{1,2}}\to FN^{R_{1,2}}/TB\) & 87.9(+11.4) & 90.7(+10.9) \\ \hline FT & \(PT\to PN\to TB\to FN/FN\) & 89.5 & 92.9 \\ ER & \(PT\to PN^{R_{1}}\to TB^{R_{1,2}}\to FN^{R_{1,2}}/FN\) & 89.1 & 93 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The mAP results of different experiments on multiple test-sets. FT is the Fine-tuning, and ER is the Experience replay. Acronyms TB, PN, PT, and FN denote TableBank, PubLayNet, PubTables-1M, and FinTabNet, respectively. The \(R\) superscripts for ER, demonstrate the index of the previous datasets contributing to the replay memory.  
learning method for table detection in documents. To the best of our knowledge, this work is the first attempt to investigate the capabilities of a continual-learning-based method in the field of document image analysis. We conduct experiments with four different settings: independent training, joint training, fine-tuning, and training with an experience replay mechanism. While the conventional independent training is considered as the upper bound for performance, results with fine-tuning are taken as the lower bound. With the employed continual learning method (ER), we achieve a significant increase of 15% on the results achieved by fine-tuning. Furthermore, the performance from our method is comparable to previous state-of-the-art dataset-specific training methods.  
Our evaluations of the modern table detection datasets demonstrate the potential to address the major challenge of relying on dataset-specific training in document image analysis. Moreover, it is an important step in developing robust table detection methods for different domains. We hope that this study serves as an inspiration for future research in continual learning for document image analysis.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.