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+*This model was released on 2021-09-30 and added to Hugging Face Transformers on 2022-10-18.*
+
+# Table Transformer
+
+<div class="flex flex-wrap space-x-1">
+<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+## Overview
+
+The Table Transformer model was proposed in [PubTables-1M: Towards comprehensive table extraction from unstructured documents](https://huggingface.co/papers/2110.00061) 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](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.*
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/table_transformer_architecture.jpeg"
+alt="drawing" width="600"/>
+
+<small> Table detection and table structure recognition clarified. Taken from the <a href="https://huggingface.co/papers/2110.00061">original paper</a>. </small>
+
+The authors released 2 models, one for [table detection](https://huggingface.co/microsoft/table-transformer-detection) in
+documents, one for [table structure recognition](https://huggingface.co/microsoft/table-transformer-structure-recognition)
+(the task of recognizing the individual rows, columns etc. in a table).
+
+This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be
+found [here](https://github.com/microsoft/table-transformer).
+
+## Resources
+
+<PipelineTag pipeline="object-detection"/>
+
+- A demo notebook for the Table Transformer can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Table%20Transformer).
+- It turns out padding of images is quite important for detection. An interesting Github thread with replies from the authors can be found [here](https://github.com/microsoft/table-transformer/issues/68).
+
+## TableTransformerConfig
+
+[[autodoc]] TableTransformerConfig
+
+## TableTransformerModel
+
+[[autodoc]] TableTransformerModel
+    - forward
+
+## TableTransformerForObjectDetection
+
+[[autodoc]] TableTransformerForObjectDetection
+    - forward