| Table Transformer | |
| Overview | |
| The Table Transformer model was proposed in PubTables-1M: Towards comprehensive table extraction from unstructured documents by | |
| Brandon Smock, Rohith Pesala, Robin Abraham. The authors introduce a new dataset, PubTables-1M, to benchmark progress in table extraction from unstructured documents, | |
| as well as table structure recognition and functional analysis. The authors train 2 DETR models, one for table detection and one for table structure recognition, dubbed Table Transformers. | |
| The abstract from the paper is the following: | |
| Recently, significant progress has been made applying machine learning to the problem of table structure inference and extraction from unstructured documents. | |
| However, one of the greatest challenges remains the creation of datasets with complete, unambiguous ground truth at scale. To address this, we develop a new, more | |
| comprehensive dataset for table extraction, called PubTables-1M. PubTables-1M contains nearly one million tables from scientific articles, supports multiple input | |
| modalities, and contains detailed header and location information for table structures, making it useful for a wide variety of modeling approaches. It also addresses a significant | |
| source of ground truth inconsistency observed in prior datasets called oversegmentation, using a novel canonicalization procedure. We demonstrate that these improvements lead to a | |
| significant increase in training performance and a more reliable estimate of model performance at evaluation for table structure recognition. Further, we show that transformer-based | |
| object detection models trained on PubTables-1M produce excellent results for all three tasks of detection, structure recognition, and functional analysis without the need for any | |
| special customization for these tasks. | |
| Table detection and table structure recognition clarified. Taken from the original paper. | |
| The authors released 2 models, one for table detection in | |
| documents, one for table structure recognition | |
| (the task of recognizing the individual rows, columns etc. in a table). | |
| This model was contributed by nielsr. The original code can be | |
| found here. | |
| Resources | |
| A demo notebook for the Table Transformer can be found here. | |
| It turns out padding of images is quite important for detection. An interesting Github thread with replies from the authors can be found here. | |
| TableTransformerConfig | |
| [[autodoc]] TableTransformerConfig | |
| TableTransformerModel | |
| [[autodoc]] TableTransformerModel | |
| - forward | |
| TableTransformerForObjectDetection | |
| [[autodoc]] TableTransformerForObjectDetection | |
| - forward |