Bug report: SFCN was not added to Merge model's beginning. You can fix this bug by modify Class Merge in ./model/merge.py.
TF_SPLERGE is an implementation for SPLERGE which is an method for tabel structure decomposition, published on ICDAR 2019 and got the best paper in table strucuture analysis (the paper has been put in the rootdir of the rep).
My blogs about this implementation is here. Another implementation based on Torch is here, which provided reference for me at the beginning.
tensorflow==2.2.0
opencv3==3.1.0
numpy==1.19.1
networkx==2.5.1
fitz==0.0.1
The models I provide are over-fitting due to the poor quantity of the data . So please run the inference.py just for a test.
If you just want to learn SPLERGE, you can only use the public dataset from ICDAR which I have put into this rep. While if you want to use this method for real work, you should prepare your own large mount dataset just like the paper's author.
Code about processing the public ICDAR 2013 dataset is in the process_data folder. You can process it from the beginning by running the proc_ICDAR2013.py. After this, you can view the label for text bounding box under check_img folder.
Codes to generate tfrds for train is in the gen_ground_truth folder. You can generate the data by running gen_gt.py.
All hyperparameters about training can be changed in split_config.py and merge_config.py under the config folder.
You must train for split stage and get a model for split which can work well firstly.
python split_train.py
After this, you can train the merge stage based on the split model. I froze the split model to train the merge model until the last. Theoretically, after training merge model for a while, you can release the split model and train the two stage together.
python merge_train.py
Run the test.py in the rootdir.