# UTTSR: A Novel Non-Structured Text Table Recognition Model Powered by Deep Learning Technology
## 4 Experiment
### Comparative Experiment  
This paper evaluated our proposed models using the ICDAR 2015, MARMOT, SC-ITSR, and PUBTABNET table recognition datasets. To compare with other existing table recognition models such as Splerge and TableNet, we present the experimental results of different models on these four datasets in Table 1.  
Table 2 shows the evaluation results of Faster RCNN, Cascade Faster R-CNN, and Cascade Faster RCNN+FPN on the SCITSR and Marmot datasets for table detection. The accuracy rate of Faster R-CNN is only 62.7%. The other two models use ResNeXt101 and ResNeXt105 as backbones, with ResNeXt105 being more accurate due to having more parameters than ResNeXt101.  
The detection accuracy of the improved Cascade Faster RCNN+FPN model is 81.40%, which is a significant improvement compared to Faster R-CNN and Cascade Faster RCNN. This demonstrates that the improved FPN+Cascade-RCNN model is highly effective and robust, with better overall performance when compared to other networks.  
For table structure recognition, we conducted experiments on datasets using the traditional algorithm + PDF, the layout parser method, and TableMaster sequence recognition, as shown in Table 3. The F1 score of TableMaster was found to be higher than that of the layout parser algorithm. Additionally, its accuracy was slightly better compared to the layout parser and the combined image with PDF information method, as presented in Table 3.  
For text line detection, we conducted experiments using DBNet, DBNet++, and Docov-based DBNet on the ICDAR2015 dataset. The performance of these models for text line recognition is presented in Table 4.  
The DO-Conv-based DBNet method outperformed DBNet and DBNet++ in terms of precision and F1 scores. Specifically, on the ICDAR2015 dataset, the DBNet based on DO-Conv had a precision of 2 percentage points higher than the original DBNet and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Methods** &
\begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} & **Recall (\%)** & **F1 (\%)** & **TEDS (\%)** \\ \hline \multirow{3}{*}{ICDAR2015} & SPLETGE & 99.2 & 98.8 & 98.99 & - \\  & UTTSR (ours) & 99.5 & 99.4 & 99.45 & - \\  & TableNet & 97.4 & 96.2 & 96.80 & - \\ \hline \multirow{3}{*}{Marmote} & SPLETGE & 98.8 & 99.0 & 98.90 & - \\  & UTTSR (ours) & 99.2 & 98.7 & 98.94 & - \\  & TableNet & 93.1 & 90.1 & 91.58 & - \\ \hline \multirow{3}{*}{PubTabnet} & SPLETGE & - & - & - & 97.1 \\  & UTTSR (ours) & - & - & - & 98.5 \\  & TableNet & - & - & - & 96.7 \\ \hline \multirow{3}{*}{SCITSR} & SPLETGE & 85.4 & 82.3 & 83.82 & - \\  & UTTSR (ours) & 95.2 & 94.8 & 95.00 & - \\ \cline{1-1}  & TableNet & 92.2 & 89.8 & 90.98 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of UTTSR, SPLETGE and TableNet on multiple datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline Image Algorithm+PDF & 90.00 & 30.36 & 72.0 \\ Layout Parser & 95.25 & 84.16 & 89.0 \\ TableMaster & **96.95** & **93.07** & **95.0** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Table structure detection method comparison.  
2.8 percentage points higher than the DBNet++. Regarding recall, the DO-Conv-based DBNet was 0.5 percentage points lower than the original DBNet and 1.5 percentage points lower than the DBNet++. In terms of F1 score, our proposed DO-Conv-based DBNet achieved a performance that was 0.5 percentage points higher than the original DBNet and 0.3 percentage points higher than the DBNet++. Overall, the DO-Conv-based DBNet method demonstrated better performance compared to the other DBNet methods.