streamlit app using Table Transformer and OCR

README.md

@@ -111,6 +111,8 @@ Currently, it contains the following demos:
 * X-CLIP ([paper](https://arxiv.org/abs/2208.02816)):
   - performing zero-shot video classification with X-CLIP [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/X-CLIP/Video_text_matching_with_X_CLIP.ipynb)
   - zero-shot classifying a YouTube video with X-CLIP [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/X-CLIP/Zero_shot_classify_a_YouTube_video_with_X_CLIP.ipynb)
+* Table Transformer ([paper](https://arxiv.org/abs/2110.00061)):
+  - detects table and recognizes table structure on image with table [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/Table%20Transformer/Using_Table_Transformer_for_table_detection_and_table_structure_recognition.ipynb)
 
 ... more to come! 🤗 
 
@@ -142,6 +144,7 @@ Btw, I was also the main contributor to add the following algorithms to the libr
 - VideoMAE by Multimedia Computing Group, Nanjing University
 - X-CLIP by Microsoft Research
 - MarkupLM by Microsoft Research
+- Table Transformer by Microsoft Research
 
 All of them were an incredible learning experience. I can recommend anyone to contribute an AI algorithm to the library!
 

Table Transformer/README.md

@@ -7,3 +7,8 @@ can be done as shown in the notebooks found in [this folder](https://github.com/
 
 The only difference is that the Table Transformer applies a "normalize before" operation, which means that layernorms are applied before,
 rather than after MLPs/attention.
+
+To automatically parse a table and turn it into a CSV file, check out [this demo](https://huggingface.co/spaces/SalML/TableTransformer2CSV) on HuggingFace Spaces based on the Table Transformer + OCR.
+
+
+![432d09f05f9178c0929729ae27b2928e](https://user-images.githubusercontent.com/31631107/197332016-de9314bc-2159-44bb-9428-ef07c6a96850.png)