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This project extracts important sections from scientific papers and converts them into text. The sections we chose to be extracted are Title, Abstract, Author/s. So far I've used UNet and OCR.The UNet is used to extract the sections.The OCR converts it into text.The complete and final version includes BERT to do the text summarization. Full tutorial here.
git clone https://dagshub.com/Eman22S/Unet-OCR-2.0.git
dvc
to track your datasetpip install dvc
dagshub
for data, model and experiement logging/tracking.pip install dagshub
dvc
as follows. Dagshub will auto generate the content in braces for you.dvc remote add origin https://dagshub.com/Eman22S/Unet-OCR-2.0.dvc
dvc remote modify origin — local auth basic
dvc remote modify origin — local user {your-username}
dvc remote modify origin — local password {your-password}
dvc pull -r origin
tesseract
on your machine.sudo apt install tesseract-ocr-all -y
Make sure you changed directory to installation-path/Unet-OCR-2.0/UNet-OCR/Pytorch-UNet
.Also make sure you’re images and masks are in installation-path/Unet-OCR-2.0/UNet-OCR/Pytorch-UNet/data/imgs
and installation-path/Unet-OCR-2.0/UNet-OCR/Pytorch-UNet/data/masks
respectively. Or run dvc pull -r origin
to use my training dataset.
python train.py --amp
2. Run prediction using the following code:
python predict.py -i {path to testset} -o {path to save generated file}
python postprocess.py -i {path to masked image} -e {path to original image} -o {path to save output}
tesseract {path to the postprocessed image} {path with name of file to save the text } --l eng
The dataset this project is trained on is of first page images of scientific papers. if you want to train on your dataset, make sure you convert it to jpg images.
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