Pytorch Implementation of "R2CNN Rotational Region CNN for Orientation Robust Scene Text Detection" paper , it is based on facebook's maskrcnn-benchmark
Check INSTALL.md for installation instructions.
1. Download icdar2015 dataset and pretrain model from maskrcnn-bencmark
cd ./tools
mkdir datasets
ln -s PATH_ICDAR2015 datasets/ICDAR2015
mkdir pretrain
cd pretrain
wget https://download.pytorch.org/models/maskrcnn/e2e_faster_rcnn_R_50_FPN_1x.pth
2. Convert annotations to COCO style
cd ./tools/ICDAR2015
python convert_icdar_to_coco.py
3. start training
cd ./tools
python train_net.py
1. Download model or use your own model
2. single image inference
cd ./tools
python inference_engine.py
- new data structure quad_bbox(x1, y1, x2, y2, x3, y3, x4, y4) is defined to replace bbox(x1, y1, x2, y2)
- an extra branch in box_head which regress offsets of 4 points
- post processor of rpn is adjusted to detect text objects
- [x]
Please consider citing this project in your publications if it helps your research. The following is a BibTeX reference. The BibTeX entry requires the url LaTeX package.
@misc{r2cnn,
author = {Yingying Jiang, Xiangyu Zhu, Xiaobing Wang, Shuli Yang, Wei Li, Hua Wang, Pei Fu, Zhenbo Luo},
title = {R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection},
conference = {ICPR2018}
year = {2017},
}