# 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).
**_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.
## Examples
See `main.py` for examples on how to draw the images.
## Local Setup
```
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.
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.
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
Feel free to contribute to this repository through Pull Requests and Issues. Reach out to me if you have any ideas surrounding this that you want to discuss!
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"description": "# Recursive Segmentation Model\n\nThe 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).\n\n**_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._\n\n## Getting Started\n\nThis repository is pushed to a PyPI distribution.\n\n## Examples\n\nSee `main.py` for examples on how to draw the images.\n\n## Local Setup\n\n```\npip install -r requirements.txt\n```\n\n## Additional Information\n\nThis 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.\n\nAt 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.\n\nLike 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!\n\n## Contributing\n\nFeel free to contribute to this repository through Pull Requests and Issues. Reach out to me if you have any ideas surrounding this that you want to discuss!\n",
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