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Pub Tables-1M: Towards comprehensive table extraction from unstructured documents Brandon Smock Rohith Pesala Robin Abraham Microsoft Redmond, WA brsmock,ropesala,robin. abraham@microsoft. com Abstract Recently, significant progress has been made applying machine learning to the problem of table structure inference and ... | tatr.pdf |
Figure 2. Illustration of the three subtasks of table extraction addressed by the Pub Tables-1M dataset. For instance, markup annotations do not encode spatial coor-dinates for cells, and they only encode logical relationships implicitly through cues such as layout [20]. Not only does this lack of explicit information ... | tatr.pdf |
(a) Oversegmented structure annotation (b) Canonical structure annotation Figure 3. In the above example, the structure annotation on the left is oversegmented, creating extra blank cells in the headers. The canonical structure annotation on the right merges these cells and captures its true logical structure. Finally,... | tatr.pdf |
Table 1. Comparison of crowd-sourced datasets for table structure recognition. Dataset Input Modality# Tables Cell Topology Cell Content Cell Location Row & Column Location Canonical Structure Table Bank [9] Image 145K ✓ Sci TSR [3] PDF∗15K ✓ ✓ Pub Tab Net [22, 23] Image 510K‡✓ ✓ ✓† Fin Tab Net [22] PDF∗113K ✓ ✓ ✓† Pub... | tatr.pdf |
Canonicalization The primary goal of the canonicaliza-tion step is to correct oversegmentation in a table's structure annotations. To do this, we need to make assumptions about a table's intended structure. As the canonicalization algo-rithm itself is relatively simple, we first describe it, then detail the assumptions ... | tatr.pdf |
Table 2. Estimated measure of oversegmentation for projected row headers (PRHs) by dataset. As PRHs are only one type of cell that can be oversegmented, this is a partial survey of the total oversegmentation in these datasets. Dataset Total Tables Investigated†Total Tables with a PRH∗Tables with an oversegmented PRH To... | tatr.pdf |
Figure 4. An example table with dilated bounding box annotations for different object classes for jointly modeling table structure recognition and functional analysis. page and table annotation for TD is shown in Fig. 2. Note that tables that span multiple pages are considered outside the scope of this work. 4. Propose... | tatr.pdf |
Table 4. Test performance of the TSR + FA models on Pub Tables-1M on TSR metrics. Test Data Model Table Category Acc Cont Gri TS Top Gri TS Cont Gri TS Loc Adj Cont Non-Canonical DETR-NC Simple 0. 8678 0. 9872 0. 9859 0. 9821 0. 9801 Complex 0. 5360 0. 9600 0. 9618 0. 9444 0. 9505 All 0. 7336 0. 9762 0. 9761 0. 9668 0.... | tatr.pdf |
8. Acknowledgments We would like to thank Pramod Sharma, Natalia Larios Delgado, Joseph N. Wilson, Mandar Dixit, John Corring, and Ching Pui WAN for helpful discussions and feedback while preparing this manuscript. References [1]Amihood Amir, Tzvika Hartman, Oren Kapah, B Riva Shalom, and Dekel Tsur. Generalized LCS. T... | tatr.pdf |
maximum number of objects in each set's training samples. Besides this, we use the same default architecture settings for each model. All of the experiments are performed using a single NVidia Tesla V100 GPU. We initialize the models with weights pre-trained on Image Net and train each model for 20 epochs using all def... | tatr.pdf |
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