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https://github.com/xhzdeng/crpn
Corner-based Region Proposal Network
https://github.com/xhzdeng/crpn
Last synced: 10 days ago
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Corner-based Region Proposal Network
- Host: GitHub
- URL: https://github.com/xhzdeng/crpn
- Owner: xhzdeng
- License: other
- Created: 2018-03-23T06:44:04.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-04-18T07:41:17.000Z (over 6 years ago)
- Last Synced: 2024-08-02T11:14:52.349Z (3 months ago)
- Language: C++
- Size: 2.95 MB
- Stars: 140
- Watchers: 5
- Forks: 44
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Corner-based Region Proposal Network
CRPN is a two-stage detection framework for multi-oriented scene text. It employs corners to estimate the possible locations of text instances and a region-wise subnetwork for further classification and regression. In our experiments, it achieves F-measure of 0.876 and 0.845 on ICDAR 2013 and 2015 respectively. The paper is available at [arXiv](https://arxiv.org/abs/1804.02690).
### Installation
This code is based on [Caffe](https://github.com/BVLC/caffe) and [py-faster-rcnn](https://github.com/rbgirshick/py-faster-rcnn). It has been tested on Ubuntu 16.04 with CUDA 8.0.
0. Clone this repository
```
git clone https://github.com/xhzdeng/crpn.git
```
1. Build Caffe and pycaffe
```
cd $CRPN_ROOT/caffe-fast-rcnn
make -j8 && make pycaffe
```
2. Build the Cython modules
```
cd $CRPN_ROOT/lib
make
```
3. Prepare your own training data directory. For convenience, it should have this basic structure.
```
$VOCdevkit/
$VOCdevkit/VOC2007 # image sets, annotations, etc.
```
And create symlinks for YOUR dataset
```
cd $CRPN_ROOT/data
ln -s [path] VOCdevkit
```
4. Download pretrained ImageNet VGG-16 model. You can find it at [Caffe Model Zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo).
5. Train with YOUR dataset
```
cd $CRPN_ROOT
./experiments/scripts/train.sh [NET] [MODEL] [DATASET] [ITER_NUM]
# NET is the network arch to use, only {vgg16} in this implemention
# MODEL is the pre-trained model you want to use to initial your weights
# DATASET points to your dataset, please refer the contents of train.sh
# IETR_NUM
```
6. Test with YOUR models
```
cd $CRPN_ROOT
./experiments/scripts/test.sh [NET] [MODEL] [DATASET]
# NET is the network arch to use, only {vgg16} in this implemention
# MODEL is the testing model
# DATASET points to your dataset, please refer the contents of test.sh
```
Test outputs are saved under:
```
output////
```
### Demo
```
cd $CRPN_ROOT
./tools/demo.py --net [NET] --model [MODEL]
# NET is the network arch to use, only {vgg16} in this implemention
# MODEL is the path of caffemodel you want to use
```
### Models
Now, you can download the pretrained model from [OneDrive](https://1drv.ms/f/s!AiAzf2_GWxxlefnWI2-umwO3R9g) or [BaiduYun](https://pan.baidu.com/s/1Ivk4v49w0oW4VzWQMMEqcQ), which is trained 100k iters on [SynthText](https://github.com/ankush-me/SynthText). I also have uploaded a testing model trained recently. It achieves an F-measure of 0.8456 at 840p resolution on ICDAR 2015, similar performance but slightly faster than we depicted in the paper.
### Citation
If you find the paper and code useful in your research, please consider citing:
@article{deng2018crpn,
Title = {Detecting Multi-Oriented Text with Corner-based Region Proposals},
Author = {Linjie Deng and Yanxiang Gong and Yi Lin and Jingwen Shuai and Xiaoguang Tu and Yufei Zhang and Zheng Ma and Mei Xie},
Journal = {arXiv preprint arXiv:1804.02690},
Year = {2018}
}