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https://github.com/mjq11302010044/RRPN

Arbitrary-Oriented Scene Text Detection via Rotation Proposals (TMM 2018)
https://github.com/mjq11302010044/RRPN

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Arbitrary-Oriented Scene Text Detection via Rotation Proposals (TMM 2018)

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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!