This is a byproduct of a university project. The project itself was about document analysis based on a documents' layout.
The ReadingBank dataset consists of boxes which are representing words. Those are clustered into lines or rows. Finally, those rows are clustered again into paragraphs using agglomerative clustering.
In order to find the closest boxes (which representing lines) a customized manhattan-distance based distance is used. This distance calculates the closest distance between two boxes. If they overlap in one dimension (x- or y-axis) the distance is 0.
Since multiple lines (horizontally and vertically) can form a single paragraph two thresholds are used.
You can find and change those the config.py-file.
Namely, they are DISTANCE_THRESHOLD and CLUSTER_THRESHOLD.
While DISTANCE_THRESHOLD is used as threshold for the distance in x-axis direction, CLUSTER_THRESHOLD is used for the distance in y-axis direction.
pip install -r requirements.txtcd src/python example.py
You can find in example.py the intended usage. The lines in focus are:
clustered_document = process(readingbank_document)
visualize_readingbank_document(
doc=clustered_document,
paragraphs_affiliation=clustered_document["paragraph_class_per_line"],
)This produces a plot like this one:
This Repository's intended use is to work with documents which are given in the format of the ReadingBanks format.
This includes (only) the layout information.
If you want to use an arbitrary document, make sure it is a json-object containing the keys: src and tgt.
Examples are given in the example.jsonl-file.
Feel free to open an issue, if you need guidance or want to discuss things.