barongeng / textfield

TextField: Learning A Deep Direction Field for Irregular Scene Text Detection (TIP 2019)

TextField: Learning A Deep Direction Field for Irregular Scene Text Detection

Introduction

The code and trained models of:

TextField: Learning A Deep Direction Field for Irregular Scene Text Detection, TIP 2019 [Paper]

Citation

Please cite the related works in your publications if it helps your research:

@article{xu2018textfield,
  title={TextField: Learning A Deep Direction Field for Irregular Scene Text Detection},
  author={Xu, Yongchao and Wang, Yukang and Zhou, Wei and Wang, Yongpan and Yang, Zhibo and Bai, Xiang},
  journal={arXiv preprint arXiv:1812.01393},
  year={2018}
}

Prerequisite

• Caffe and SynthText pretrained model [Link]

• Datasets: [Total-Text], [ICDAR2015]

• OpenCV 3.4.3

• MATLAB

Usage

1. Install Caffe

cp Makefile.config.example Makefile.config
# adjust Makefile.config (for example, enable python layer)
make all -j16
# make sure to include $CAFFE_ROOT/python to your PYTHONPATH.
make pycaffe

Please refer to Caffe Installation to ensure other dependencies.

2. Data and model preparation

# download datasets and pretrained model then
mkdir data && mv [your_dataset_folder] data/
mkdir models && mv [your_pretrained_model] models/

3. Training scripts

# an example on Total-Text dataset
cd examples/TextField/
python train.py --gpu [your_gpu_id] --dataset total --initmodel ../../models/synth_iter_800000.caffemodel

4. Evaluation scripts

# an example on Total-Text dataset
cd evaluation/total/
./eval.sh

Results and Trained Models

Total-Text

Recall	Precision	F-measure	Link
0.816	0.824	0.820	[Google drive]

*lambda=0.50 for post-processing

ICDAR2015

Recall	Precision	F-measure	Link
0.811	0.846	0.828	[Google drive]

*lambda=0.75 for post-processing