https://github.com/yxgong0/DTR

A network for irregular text recognition.
https://github.com/yxgong0/DTR

Last synced: 10 days ago
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A network for irregular text recognition.

• Host: GitHub
• URL: https://github.com/yxgong0/DTR
• Owner: yxgong0
• Created: 2019-08-23T05:31:48.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2020-12-11T06:12:52.000Z (almost 4 years ago)
• Last Synced: 2024-05-21T17:10:20.246Z (6 months ago)
• Language: Python
• Size: 38.1 KB
• Stars: 27
• Watchers: 4
• Forks: 5
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# Deformable Text Recognition

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](https://github.com/meijieru/crnn.pytorch) and [Deformable-ConvNets](https://github.com/msracver/Deformable-ConvNets). For details, please refer to [our paper](https://ieeexplore.ieee.org/abstract/document/9064428).

## Requirements

* [Python 3.6](https://www.python.org/)

* [PyTorch 1.0](https://pytorch.org/)

* [TorchVision](https://pypi.org/project/torchvision/)

* [Numpy](https://pypi.org/project/numpy/)

* [Six](https://pypi.org/project/six/)

* [Scipy](https://pypi.org/project/scipy/)

* [LMDB](https://pypi.org/project/lmdb/)

* [Pillow](https://pypi.org/project/Pillow/) 

* [warp-ctc-pytorch](https://github.com/baidu-research/warp-ctc)

## Data Preparation

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 

* [Baidu Netdisk](https://pan.baidu.com/s/10NHaiJaRO1TpMON-OgcPFQ)

* [Google Drive](https://drive.google.com/open?id=1z48dRxyFVCjokXYRQtFx6k3bGgh52EzL)

* [Onedrive](https://1drv.ms/u/s!Aoxs3QdtBgqEssZAw5YBe_iOYPiyuw?e=SiXnrE)

which should be contained in the test_data folder.

## Train and Test

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.

## Citation

    @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}

    }