Tools for extract figure, table, text,... from a pdf document

$   pip install -r requirements.txt

Requirment

• CUDA=10.1
• PyTorch>=1.7.0

How to install CUDA 10.1 can be found here: https://developer.nvidia.com/cuda-10.1-download-archive-base

How to install PyTorch can be found here: https://pytorch.org/

Afer installed above package, follow the instructions below to install detectron2:

$   git clone https://github.com/facebookresearch/detectron2.git
$   git checkout 8e3effc
$   python -m pip install -e detectron2

Follow the instructions below:

$   git clone -b dev https://github.com/Wild-Rift/Document-Layout-Analysis.git
$   cd Document-Layout-Analysis

We use IBM Publaynet dataset for training and testing.

It includes 358,353 images, 335,703 training images, 11,245 validation images and 11,405 test images. The category-id label mapping of this dataset is:

After download and extract dataset, please put it in datasets directory. The directories should be arranged like this:

root
├── mmdet
├── tools
├── configs
├── output
│   ├──...
│
├── datasets
│   ├── publaynet
│   │   ├── test/
│   │   ├── train/
│   │   ├── val/
│   │   ├── train.json
│   │   ├── val.json

Document-Layout-Analysis support training on two models: Faster-RCNN and Mask-RCNN

$   CONFIG_FILE='configs/faster_rcnn_R_101_FPN_3x.yaml'      # if use Faster-RCNN model
$   CONFIG_FILE='configs/mask_rcnn_R_101_FPN_3x.yaml'        # if use Mask-RCNN model

If you want to inspect model's structures, go to configs directory

If you want to training on 8 GPU, run:

$   python train.py --num-gpus 8 --config-file CONFIG_FILE

If you want to training on 1 GPU, you may need to change some parameters, run:

$   python train.py --num-gpus 1 \
    --config-file CONFIG_FILE \
    SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025

Checkpoints of model will be store in output directory after each epoch.