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.

This code is based on Caffe and 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.

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/<experiment directory>/<dataset name>/<network snapshot name>/
   

```
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
```

Now, you can download the pretrained model from OneDrive or BaiduYun, which is trained 100k iters on 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.

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}
}