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https://github.com/mjq11302010044/RRPN
Arbitrary-Oriented Scene Text Detection via Rotation Proposals (TMM 2018)
https://github.com/mjq11302010044/RRPN
Last synced: 10 days ago
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Arbitrary-Oriented Scene Text Detection via Rotation Proposals (TMM 2018)
- Host: GitHub
- URL: https://github.com/mjq11302010044/RRPN
- Owner: mjq11302010044
- License: other
- Created: 2017-09-06T10:33:46.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2020-10-16T02:18:10.000Z (about 4 years ago)
- Last Synced: 2024-08-02T11:14:48.999Z (3 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 10.2 MB
- Stars: 437
- Watchers: 17
- Forks: 142
- Open Issues: 40
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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- awesome-ocr - Arbitrary-Oriented Scene Text Detection via Rotation Proposals
README
### Paper source
# Arbitrary-Oriented Scene Text Detection via Rotation Proposals
https://arxiv.org/abs/1703.01086
### News
We update RRPN in pytorch 1.0! View **https://github.com/mjq11302010044/RRPN_plusplus** for more details. Text Spotter f-measure results are 89.5 % in IC15, 92.0\% in IC13. The testing speed can reach 13.3 fps in IC13 with input shorter size of 640px !
### License
RRPN is released under the MIT License (refer to the LICENSE file for details). This project is for research purpose only, further use for RRPN should contact authors.
### Citing RRPN
If you find RRPN useful in your research, please consider citing:
@article{Jianqi17RRPN,
Author = {Jianqi Ma and Weiyuan Shao and Hao Ye and Li Wang and Hong Wang and Yingbin Zheng and Xiangyang Xue},
Title = {Arbitrary-Oriented Scene Text Detection via Rotation Proposals},
journal = {IEEE Transactions on Multimedia},
volume={20},
number={11},
pages={3111-3122},
year={2018}
}
### Contents
1. [Requirements: software](#requirements-software)
2. [Requirements: hardware](#requirements-hardware)
3. [Basic installation](#installation-sufficient-for-the-demo)
4. [Demo](#demo)
5. [Beyond the demo: training and testing](#beyond-the-demo-installation-for-training-and-testing-models)
### Requirements: software
1. Requirements for `Caffe` and `pycaffe` (see: [Caffe installation instructions](http://caffe.berkeleyvision.org/installation.html))
**Note:** Caffe *must* be built with support for Python layers!
```make
# In your Makefile.config, make sure to have this line uncommented
WITH_PYTHON_LAYER := 1
# Unrelatedly, it's also recommended that you use CUDNN
USE_CUDNN := 1
```
You can download my [Makefile.config](http://www.cs.berkeley.edu/~rbg/fast-rcnn-data/Makefile.config) for reference.
2. Python packages you might not have: `cython`, `python-opencv`, `easydict`
### Requirements: hardware
1. For training the end-to-end version of RRPN with VGG16, 4~5G of GPU memory is sufficient (using CUDNN)
### Installation (sufficient for the demo)
1. Clone the RRPN repository
```Shell
# git clone https://github.com/mjq11302010044/RRPN.git
```
2. We'll call the directory that you cloned RRPN into `RRPN_ROOT`
3. Build the Cython modules
```Shell
cd $RRPN_ROOT/lib
make
```
4. Build Caffe and pycaffe
```Shell
cd $RRPN_ROOT/caffe-fast-rcnn
# Now follow the Caffe installation instructions here:
# http://caffe.berkeleyvision.org/installation.html
# If you're experienced with Caffe and have all of the requirements installed
# and your Makefile.config in place, then simply do:
make -j4 && make pycaffe
```
5. Download pre-computed RRPN detectors
```Shell
Trained VGG16 model download link: https://drive.google.com/open?id=0B5rKZkZodGIsV2RJUjVlMjNOZkE
```
Then move the model into `$RRPN_ROOT/data/faster_rcnn_models`.
### Demo
*After successfully completing [basic installation](#installation-sufficient-for-the-demo)*, you'll be ready to run the demo.
To run the demo
```Shell
cd $RRPN_ROOT
python ./tools/rotation_demo.py
```
The txt results will be saved in `$RRPN_ROOT/result`
### Beyond the demo: installation for training and testing models
You can use the function `get_rroidb()` in `$RRPN_ROOT/lib/rotation/data_extractor.py` to manage your training data:
Each training sample should be managed in a python dict like:
im_info = {
'gt_classes': # Set to 1(Only text)
'max_classes': # Set to 1(Only text)
'image': # image path to access
'boxes': # ground truth box
'flipped' : # Flip an image or not (Not implemented)
'gt_overlaps' : # overlap of a class(text)
'seg_areas' : # area of an ground truth region
'height': # height of an image data
'width': # width of an image data
'max_overlaps' : # max overlap with each gt-proposal
'rotated': # Random angle to rotate an image
}
*Then assign your database to the variable 'roidb' in __main__ function of `$RRPN_ROOT/tools/train_net.py`*
``` In $RRPN_ROOT/tools/train_net.py
116: roidb = get_rroidb("train") # change to your data manage function
```
### Download pre-trained ImageNet models
Pre-trained ImageNet models can be downloaded for the networks described in the paper: VGG16.
```Shell
cd $RRPN_ROOT
./data/scripts/fetch_imagenet_models.sh
```
VGG16 comes from the [Caffe Model Zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo), but is provided here for your convenience.
ZF was trained at MSRA.
Then you can train RRPN by typing:
```
./experiment/scripts/faster_rcnn_end2end.sh [GPU_ID] [NET] rrpn
```
[NET] usually takes `VGG16`
Trained RRPN networks are saved under:(We set the directory to './' by default.)
```
./
```
One can change the directory in variable `output_dir` in `$RRPN_ROOT/tools/train_net.py`
Any question about this project please send message to Jianqi Ma([email protected]), and enjoy it!