TextBoxes++ with PyTorch

The implementation of TextBoxes++ with PyTorch.

Requirement

pip install --upgrade git+https://github.com/jjjkkkjjj/pytorch_SSD.git
conda install lxml
conda install -c conda-forge shapely

Pre-train

• First, download SynthText dataset from official.

• Second, convert gt.mat into annotation xml files using synthtext_generator.py.

  python synthtext_generator.py {path} -id SynthText

  usage: synthtext_generator.py [-h] [-in IMAGE_DIRNAME] [-sm] [-e ENCODING]
                                path
  
  Generate Synthtext's annotation xml file
  
  positional arguments:
    path                  directory path under 'SynthText'(, 'licence.txt')
  
  optional arguments:
    -h, --help            show this help message and exit
    -id IMAGE_DIRNAME, --image_dirname IMAGE_DIRNAME
                          image directory name including 'gt.mat'
    -sm, --skip_missing   Wheter to skip missing image
    -e ENCODING, --encoding ENCODING
                          encoding

• Train. See demo/pre-train-SynthText.ipynb.

• You can download pre-trained model from here.

• Pre-trained model's output example;

Train ICDAR2015

• First, download dataset from official.

• Second, place annotation .txt and .jpg like this;

  ├── Annotations (place .txt)
  └── Images (place .jpg)

• Train. See demo/train-ICDAR2015.ipynb.

• You can download pre-trained model from here.

• ICDAR's model output example;

Convert png to jpg for Born Digital Images

$ python png2jpg ~/data/text/Born-Digital-Images/Images/ -d
Converting...	100.0%	[307/307]
finished

Reference

SynthText

COCO-text

COCO-text api

DDI-100

DDI-100 api