# Fi-Fo Detector: Figure and Formula Detection Using Deformable Networks
## 4 Experimental Results
### Results and Discussions  
We present an ablation study, which covers the potential of Fi-Fo image representation in comparison to raw image representation along with analysis of deformable networks with regards to non-deformable counterparts. Moreover, we present a comparative analysis to establish the utility and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **ICDAR-2017 POD** & \
\begin{tabular}{c} **ICDAR-2017 POD** \\ **(Corrected)** \\ \end{tabular} &
\begin{tabular}{c} **\# of Files** \\ **Modified** \\ \end{tabular} \\ \hline Figure & 2939 & 2912 & 135 \\ Formula & 5427 & 5463 & 156 \\ Table & 1016 & 1053 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of ICDAR-2017 POD (corrected) with class-wise comparison and modifications in the ICDAR-2017 POD dataset. effectiveness of the ICDAR-2017 POD (corrected) dataset with the existing state-of-the-art systems. We also evaluate the deformable variants of Faster-RCNN, R-FCN, and FPN. FPN forms the basis of the Fi-Fo detector as it comprehensively outperformed other deformable variants, owing to its multi-scale detection capabilities. Using the ICDAR-2017 POD dataset, visual results looked convincing, as both formulas and figures were detected properly, but numbers were surprisingly not up to the expectations keeping in mind the potential of FPNs implemented with deformable convolution, as shown in Table 2. On an IoU threshold of 0.6, formulas were detected with the precision and recall of 0.909 and 0.927, along with an f1-score and average precision (AP) of 0.918 and 0.911, respectively. Once the IoU threshold was increased to 0.8, precision and recall dropped to 0.856 and 0.878 with an f1-score and AP of 0.867 and 0.854, respectively. Considering figures, the obtained numbers were 0.918, 0.883, 0.90, and 0.894 in terms of precision, recall, f1-score, and average precision, respectively. For the IoU threshold of 0.8, precision, recall, and f1-score went down to 0.871, 0.838, and 0.854, respectively. As exhibited in Figure 5, the Fi-Fo detector is working fine, correctly detecting the page objects, i.e., figures and formulas in particular, but the performance is not reflected in numbers. Upon investigating the results, we discovered the irregularities and inconsistencies in the original annotations available for the ICDAR-2017 POD dataset. There were clear examples of missing annotations for page objects, as the case in Figure 5b where annotations for formulas were missing. Confusions between figure and table annotations are briefly shown in Figure 5d. Figure 5c establishes the case of inconsistent labelling for figure annotations. There were examples of over-segmented ground-truth where captions or text-lines were annotated along with figure or table, as shown in Figure 4c. These inconsistencies have been already discussed in detail in Section 4.1.2. Problems in the original annotations of the ICDAR-2017 POD dataset led us to update the annotations, which included removal of discrepancies, confusions, along with the addition of missing labels. After addressing the problems found in the ICDAR-2017 POD dataset, a clean dataset is presented as ICDAR-2017 POD (corrected). To establish a fair comparison with existing state-of-the-art methods, it is necessary to present their results on ICDAR-2017 POD (corrected). [PERSON] et al. 's [9] method is not an end-to-end system, which means it is a combination of trainable and heuristic-based parts. Therefore, it is almost impossible to replicate the system performance by building it up from scratch. Upon request, the authors [9] excused to provide us their system because of the system's complexities but provided the results on the ICDAR-2017 POD dataset for comparison. It limits the scope of comparison on the corrected dataset, as both the system and results on the corrected dataset from the [PERSON] et al. [9] were not accessible. So, in the given circumstances, we opted for the best possible way