This software implements the Deformable Convolutional Recurrent Neural Network, a combination of of Convolutional Recurrent Neural Network, Deformable Convolutional Networks and Residual Blocks. Some of the codes are from crnn.pytorch and Deformable-ConvNets. For details, please refer to our paper.
Please convert your own training dataset to LMDB format. The testing images should be in one folder with a txt file, and the txt file contains some lines which contains the filename and the label, formatted as:
img_001.png, "word"
Each line should end with '\n'. You can also use the data provided by us from
which should be contained in the test_data folder.
To train a new model, simply execute python train.py --lmdb_paths {train_path_list} --cuda. If you need to set other parameters, explore train.py for details.
To test a trained model, you need to explore and execute eval.py.
@inproceedings{deng2019focus,
title={Focus-Enhanced Scene Text Recognition with Deformable Convolutions},
author={Deng, Linjie and Gong, Yanxiang and Lu, Xinchen and Yi, Xin and Ma, Zheng and Xie, Mei},
booktitle={2019 IEEE 5th International Conference on Computer and Communications (ICCC)},
pages={1685--1689},
year={2019},
organization={IEEE}
}