Update About.py

About.py

@@ -19,7 +19,7 @@ st.markdown(
     needs to be queried for all samples which is computationally/financially [expensive](https://cloud.google.com/vision/pricing). Here, we show that the documents
     can be preprocessed using just 4% of the total OCR queries. 
     
-    👈 Select  **Denoise** in the sidebar to see document preprocessing with 100\%, 8\% and 4\% budget OCR query budget. 
+    👈 Select  **Denoise** in the sidebar to see document preprocessing with 100\%, 8\% and 4\% OCR query budget. 
     """
 )
 
@@ -31,38 +31,4 @@ st.markdown(
     # ### See more complex demos
     # - Use a neural net to [analyze the Udacity Self-driving Car Image
     #     Dataset](https://github.com/streamlit/demo-self-driving)
-    # - Explore a [New York City rideshare dataset](https://github.com/streamlit/demo-uber-nyc-pickups)
-
-# st.write("")
-# st.write("")
-# st.write("")
-
-# st.markdown("#####  This app allows you to compare, from a given picture, the results of different solutions:")
-# st.markdown("##### *EasyOcr, PaddleOCR, MMOCR, Tesseract*")
-# st.write("")
-# st.write("")
-
-# st.markdown(''' The 1st step is to choose the language for the text recognition (not all solutions \
-# support the same languages), and then choose the picture to consider. It is possible to upload a file, \
-# to take a picture, or to use a demo file. \
-# It is then possible to change the default values for the text area detection process, \
-# before launching the detection task for each solution.''')
-# st.write("")
-
-# st.markdown(''' The different results are then presented. The 2nd step is to choose one of these \
-# detection results, in order to carry out the text recognition process there. It is also possible to change \
-# the default settings for each solution.''')
-# st.write("")
-
-# st.markdown("###### The recognition results appear in 2 formats:")
-# st.markdown(''' - a visual format resumes the initial image, replacing the detected areas with \
-# the recognized text. The background is + or - strongly colored in green according to the \
-# confidence level of the recognition.
-#     A slider allows you to change the font size, another \
-# allows you to modify the confidence threshold above which the text color changes: if it is at \
-# 70% for example, then all the texts with a confidence threshold higher or equal to 70 will appear \
-# in white, in black otherwise.''')
-
-# st.markdown(" - a detailed format presents the results in a table, for each text box detected. \
-# It is possible to download this results in a local csv file.")
-
+    # - Explore a [New York City rideshare dataset](https://github.com/streamlit/demo-uber-nyc-pickups)