zdlcaffe/wsrtcb

Scene Text Detection with Fully Convolutional Neural Networks

1. Introduction

This project includes the text detection source code and trained model about the word stroke region and text center block.

2. Installation

• Clone the repo

git clone [email protected]:zdlcaffe/WSRTCB.git  

• Then you can do as follow:

cd ${WSRTCB_root/Train_WSR_TCB/caffe}  
make –j  
make pycaffe 

3. Testing

3.1 Generate WSR/TCB score map

• Download the TD_MKEI_Word.caffemodel, trained on KAIT dataset.
• Then you can do as follow:

cd ${WSRTCB_root/}  
mkdir snapshot  
mkdir pre_model

• put TD_MKEI_Word.caffemodel to the fold of ${WSRTCB_root/Train_WSR_TCB/ snapshot}.

• Suppose you have downloaded the MSRA-TD500 dataset, execute the following commands to test the model on MSRA-TD500. Then you can do as follow:

cd ${WSRTCB_root/Train_WSR_TCB/demo}  
python Demo.py  

3.2 There are some samples:

3.3 Threshold WSR/TCB maps:

You can do as follow:

cd ${WSRTCB_root/Text_Demo}  
python fuse_thred	.py  

3.4 Generate detection results

You can do as follow:

cd ${WSRTCB_root/Text_Demo}  
python Demo_region_word.py

3.5 There are some samples:

4. Training

Download the pretrained model vgg16convs.caffemodel, and put it to ${WSRTCB_root/Train_WSR_TCB/pre_model}

4.1 Generate your map

Scripts for generating ground truth have been provided in the label_generate directory. It not hard to write a converting script for your own dataset.

4.2 Train your own model

Modify ${WSRTCB_root/Train_WSR_TCB/model/TD_MKEI_Word.py} to configure your dataset name and dataset path like:
......
data_params['root'] = 'data/MKEIWord'
data_params['source'] = "MKEI_Word.lst"
......

4.3 Start training

You can do as follow:

cd ${WSRTCB_root/Train_WSR_TCB}  
sh ./train.sh 

Citation

Use this bibtex to cite this repository:

@article{liu2019scene,
  title={Scene text detection with fully convolutional neural networks},
  author={Liu, Zhandong and Zhou, Wengang and Li, Houqiang},
  journal={Multimedia Tools and Applications},
  pages={1--23},
  year={2019},
  publisher={Springer}
}

Acknowlegement