xyseg 0.0.2

Recursive Segmentation Algorithm

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). 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 (https://pypi.org/project/xy-segmentation/). Get started by running the following command:

pip install xy-segmentation

Example usage:

ifile = "examples/images/apple_iphone-13_manual.jpg"
img = Image.open(ifile)

draw = ImageDraw.Draw(img, "RGBA")
for crop in segment_pdf_image(img):
    draw.rectangle(
        crop.bounding_box, outline=(255, 0, 0), width=3, fill=(0, 127, 255, 80)
    )

img.show()

Examples

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 and images folder under examples were grabbed from here.

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 (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

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!