yangjingyuan / recursive-cnns

Khurram Javed, Faisal Shaifait "Real-time Document Localization in Natural Images by Recursive Application of a CNN"

Paper available at : https://khurramjaved96.github.io

Demo

To test the system, you can use the pretrained models by:

usage: python detectDocument.py [-i IMAGEPATH] [-o OUTPUTPATH]
                         [-rf RETAINFACTOR] [-cm CORNERMODEL]
                         [-dm DOCUMENTMODEL]

For example:

python detectDocument.py -i TrainedModel/img.jpg -o ./result.jpg -rf 0.85

would run the pretrained model on the sample image in the repository.

Training Code

Training code is mostly for reference only. It's not well documented or commented and it would be easier to re-implement the model from the paper than using this code. However I will be refactoring the code in the coming days to make it more accesible.

Self-collected dataset can be downloaded from : https://drive.google.com/drive/folders/0B9Sr0v9WkqCmekhjTTY2aV9hUmM?usp=sharing

Email : [email protected] in-case of any queries.

Dataset format for training

For training, the dataset should be in following format:

1. .npy dumps of images. An example shape can be 10000x32x32. There should be two files; 1 for training and one for validation.
2. .npy dumps for ground truth. For document detector, shape should be 10000x8. For corner detector, it should be 100000x2.