# Rethinking Learnable Proposals for Graphical Object Detection in Scanned Document Images
## 2 Related Work
### Related Datasets  
Table detection and structure recognition is a well studied area, and many standard benchmark datasets are available: UNLV [40], Marmot [41], ICDAR-13 [25], ICDAR-POD-17 [5], ICDAR-19 [42], and TableBank [43]. UNLV is one of the earliest datasets in the document object detection domain. The dataset consists of almost 10,000 document images. However, only 427 of them contain tables, and hence we only list the images containing tables in Table 2. Marmot, on the other hand, is one the largest early datasets for table detection in document images. It was introduced by Peking University and consists of 2000 images for which a ratio of almost 1:1 is maintained between the positive and negative samples. The original dataset suffered from incorrect ground truth annotations, hence we list the corrected version of the dataset from [24], which has 1967 images, in Table 2. ICDAR-2013 [25] is another popular dataset for both table detection and structure recognition. The dataset was constructed by converting PDF files to images. The dataset has 229 training images and 233 validation images. ICDAR-2017-POD (Page Object Detection) [5] is another dataset that, along with tables, also has information for the boundaries of formulas and figures. The dataset consists of 2417 images in total, where 1600 are for training and 817 are for validation. ICDAR-19 [42] is the latest dataset to come out of the ICDAR in 2019. The dataset consists of two types of document images: modern and historical. The modern set of documents is collected from scientific and commercial documents, whereas the historical documents are a collection of images of handwritten documents. TableBank, introduced by [PERSON] et al. [43], is one of the most well known datasets in the table detection literature. The dataset has 417,000 training document images pooled from word and latex documents. It has 2000 images for both validation and test. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Approach** & **Methods** & **Advantages** & **Limitations** & **Results** \\ \hline \multirow{3}{*}{CDeCNet [37]} & ResNext backbone with & High-resolution backbone, & \multirow{3}{*}{Slow, memory, intensive} & \multirow{3}{*}{State-of-the-art results at high computational costs} \\  & Cascade Mask R-CNN & & & \\  & detection head and & & & \\  & deformable convolutions & that address the issue of & & \\  & & the limited range scale & & \\ \hline \multirow{3}{*}{SR-CNN r101} & \multirow{3}{*}{Sparse R-CNN with a & Has a balanced, strong & \multirow{3}{*}{As the proposal boxes} & \multirow{3}{*}{Close to state-of-the-art} \\  & & & backbone in comparison & & \\ \cline{1-1}  & & ResNet-101 backbone & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & Transformer based & & class to learn the & \\ \cline{1-1}  & & detection backbones. & \multirow{3}{*}{Learnable proposals} & \multirow{3}{*}{Learnable proposal feature} \\ \cline{1-1}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Datasets** & **Labels** & **Training** & **Validation** & **Test** \\ \hline UNLV & T & 427 & - & - \\ Marmot & T & 1967 & - & - \\ ICDAR-13 & T & 229 & 233 & - \\ ICDAR-POD-17 & T & 1600 & 817 & - \\ ICDAR-19 & T & 1200 & 439 & - \\ TableBank (word + latex) & T & 417 K & 2 K & 2 K \\ IIIT-AR-13k & T, F, L, S, and NI & 9 K & 2 K & 2 K \\ PubLayNet & T, F, Ti, Tx, and L & 340 K & 11 K & 11 K \\ DocBank & T, Ab, Au, C, D, E, F, Fo, & 400 K & 50 K & 50 K \\  & L, P, R, S, and Ti & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics of related datasets. T: indicates table, F: indicates figure, L: indicates list, S: indicates signature, NI: indicates natural images, Ti: indicates titles, Tx: indicates text, Ab: indicates abstract, Au: indicates author, C: indicates caption, D: indicates date, E: indicates equation, Fo: indicates footer, P: indicates paragraph, R: indicates reference, S: indicates sections.