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Arbitrary-Oriented Scene Text Detection via Rotation Proposals

Arbitrary-Oriented Scene Text Detection via Rotation Proposals


Source code of RRPN ---- Arbitrary-Oriented Scene Text Detection via Rotation Proposals

Citing RRPN

If you find RRPN useful in your research, please consider citing:

    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},
    Year = {2018},

To run the demo

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

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.


VGG16 comes from the Caffe 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