update README, need to add images

README.md

@@ -1,16 +1,22 @@
 # Recursive Segmentation Model
 
-The ideas presented in this repository are largely based off the original paper from 1995: _Recursive XY cut using bounding boxes of connected components_ (https://ieeexplore.ieee.org/document/602059).
+The ideas presented in this repository are largely based off the original paper from 1995: _Recursive XY cut using bounding boxes of connected components_ (https://ieeexplore.ieee.org/document/602059). It's a super lightweight segmentation algorithm with no ML components so it also segments extremely fast and can be done in parallel too (more to come on this front).
 
 **_Disclaimer_**: _This is an unbenchmarked segmentation model. It works decently well for documents at first glance and will be extended to general images in the near future. I also need to find a better name for this package._
 
 ## Getting Started
 
-This repository is pushed to a PyPI distribution.
+This repository is pushed to a PyPI distribution (https://pypi.org/project/xy-segmentation/). Get started by running the following command:
+
+```
+pip install xy-segmentation
+```
 
 ## Examples
 
-See `main.py` for examples on how to draw the images.
+See `main.py` or `ex.ipynb` for examples on how to draw the images.
+
+Examples from the `pdfs` folder under `examples` were grabbed from [here](https://www.princexml.com/samples/) and `images` folder under `examples` were grabbed from [here](https://github.com/AIM3-RUC/MPMQA).
 
 ## Local Setup
 
@@ -20,10 +26,12 @@ pip install -r requirements.txt
 
 ## Additional Information
 
-This algorithm works particularly well with documents that have a lot of diagrams and that are well spaced. It performs poorly on documents that are purely text-based perform poorly.
+This algorithm works particularly well with documents that have a lot of diagrams and that are well spaced. It performs poorly on documents that are purely text-based (but there is usually no need to segment documents that are completely text-based just throw it into RAG directly). It could be interesting to detect situations like this and skip the segmentation step entirely for these sorts of pages.
 
 At the moment, I am looking to build out an ML model to determine when to split chunks in the page. The main principle would be to train a seq2seq model that outputs a binary sequence. The sequence input is the slices of the image and the output is a binary sequence where a 1 represents a split in the image and 0 otherwise.
 
+### Limitations
+
 Like any bounding box segmentation algorithm, the main limitation is the shape of the segmentation. Edge cases arise when the input image is not necessarily framed in a grid-shape. Take an example where an image contains "L" shaped objects. This makes it impossible to segment out the "L" shaped object defined by a bounding box. If anyone has any ideas on how to improve this, please feel free to suggest!
 
 ## Contributing