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## Examples
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See `main.py` or `ex.ipynb` for examples on how to draw the images.
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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.
+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. Basic training code setup can be found on my other [branch](https://github.com/johnathanchiu/recursive-segmentation/tree/jchiu/model-training-code/model).
### 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!
+For largely text-based PDFs, the results can look like this.
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+
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+I'm still looking for a solution so feel free to suggest any if you have ideas.
+
## 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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