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https://github.com/MhLiao/TextBoxes_plusplus
TextBoxes++: A Single-Shot Oriented Scene Text Detector
https://github.com/MhLiao/TextBoxes_plusplus
ocr scene-text scene-text-detection scene-text-recognition
Last synced: 10 days ago
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TextBoxes++: A Single-Shot Oriented Scene Text Detector
- Host: GitHub
- URL: https://github.com/MhLiao/TextBoxes_plusplus
- Owner: MhLiao
- License: other
- Created: 2018-01-29T06:36:47.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2023-10-22T00:30:42.000Z (about 1 year ago)
- Last Synced: 2024-08-02T11:15:31.477Z (3 months ago)
- Topics: ocr, scene-text, scene-text-detection, scene-text-recognition
- Language: C++
- Size: 3.51 MB
- Stars: 954
- Watchers: 41
- Forks: 279
- Open Issues: 56
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# TextBoxes++: A Single-Shot Oriented Scene Text Detector
### Introduction
This is an application for scene text detection (TextBoxes++) and recognition (CRNN).
TextBoxes++ is a unified framework for oriented scene text detection with a single network. It is an extended work of [TextBoxes](https://github.com/MhLiao/TextBoxes). [CRNN](https://github.com/bgshih/crnn) is an open-source text recognizer.
The code of TextBoxes++ is based on [SSD](https://github.com/weiliu89/caffe/tree/ssd) and [TextBoxes](https://github.com/MhLiao/TextBoxes). The code of CRNN is modified from [CRNN](https://github.com/bgshih/crnn).
For more details, please refer to our [arXiv paper](https://arxiv.org/abs/1801.02765).
### Citing the related works
Please cite the related works in your publications if it helps your research:
@article{Liao2018Text,
title = {{TextBoxes++}: A Single-Shot Oriented Scene Text Detector},
author = {Minghui Liao, Baoguang Shi and Xiang Bai},
journal = {{IEEE} Transactions on Image Processing},
doi = {10.1109/TIP.2018.2825107},
url = {https://doi.org/10.1109/TIP.2018.2825107},
volume = {27},
number = {8},
pages = {3676--3690},
year = {2018}
}
@inproceedings{LiaoSBWL17,
author = {Minghui Liao and
Baoguang Shi and
Xiang Bai and
Xinggang Wang and
Wenyu Liu},
title = {TextBoxes: {A} Fast Text Detector with a Single Deep Neural Network},
booktitle = {AAAI},
year = {2017}
}
@article{ShiBY17,
author = {Baoguang Shi and
Xiang Bai and
Cong Yao},
title = {An End-to-End Trainable Neural Network for Image-Based Sequence Recognition
and Its Application to Scene Text Recognition},
journal = {{IEEE} TPAMI},
volume = {39},
number = {11},
pages = {2298--2304},
year = {2017}
}
### Contents
1. [Requirements](#requirements)
2. [Installation](#installation)
3. [Docker](#docker)
4. [Models](#models)
5. [Demo](#demo)
6. [Train](#train)
### Requirements
**NOTE** There is partial support for a docker image. See `docker/README.md`. (Thank you for the PR from [@mdbenito](https://github.com/mdbenito))
Torch7 for CRNN;
g++-5; cuda8.0; cudnn V5.1 (cudnn 6 and cudnn 7 may fail); opencv3.0
Please refer to [Caffe Installation](http://caffe.berkeleyvision.org/install_apt.html) to ensure other dependencies;
### Installation
1. compile TextBoxes++ (This is a modified version of caffe so you do not need to install the official caffe)
```Shell
# Modify Makefile.config according to your Caffe installation.
cp Makefile.config.example Makefile.config
make -j8
# Make sure to include $CAFFE_ROOT/python to your PYTHONPATH.
make py
```
2. compile CRNN (Please refer to [CRNN](https://github.com/bgshih/crnn) if you have trouble with the compilation.)
```Shell
cd crnn/src/
sh build_cpp.sh
```
### Docker
(Thanks for the PR from [@idotobi](https://github.com/idotobi))
Build Docke Image
docker build -t tbpp_crnn:gpu .
This can take +1h, so go get a coffee ;)
Once this is done you can start a container via `nvidia-docker`.
nvidia-docker run -it --rm tbpp_crnn:gpu bash
To check if the GPU is available inside the docker container you can run `nvidia-smi`.
It's recommendable to mount the `./models` and `./crnn/model/` directories to include the downloaded [models](#models).
nvidia-docker run -it \
--rm \
-v ${PWD}/models:/opt/caffe/models \
-v ${PWD}/crrn/model:/opt/caffe/crrn/model \
tbpp_crnn:gpu bash
For convenince this command is executed when running `./run.bash`.
### Models
1. pre-trained model on SynthText (used for training):
[Dropbox](https://www.dropbox.com/s/kpv17f3syio95vn/model_pre_train_syn.caffemodel?dl=0);
[BaiduYun](https://pan.baidu.com/s/1htV2j4K)
2. model trained on ICDAR 2015 Incidental Text (used for testing):
[Dropbox](https://www.dropbox.com/s/9znpiqpah8rir9c/model_icdar15.caffemodel?dl=0);
[BaiduYun](https://pan.baidu.com/s/1bqekTun)
Please place the above models in "./models/"
If your data is hugely different from ICDAR 2015 Incidental Text，you'd better train it on your own data based on the pre-trained model on SynthText.
3. CRNN model:
[Dropbox](https://www.dropbox.com/s/kmi62qxm9z08o6h/model_crnn.t7?dl=0);
[BaiduYun](https://pan.baidu.com/s/1jJwmneI)
Please place the crnn model in "./crnn/model/"
### Demo
Download the ICDAR 2015 model and place it in "./models/"
```Shell
python examples/text/demo.py
```
The detection results and recognition results are in "./demo_images"
### Train
#### Create lmdb data
1. convert ground truth into "xml" form: [example.xml](./data/example.xml)
2. create train/test lists (train.txt / test.txt) in "./data/text/" with the following form:
path_to_example1.jpg path_to_example1.xml
path_to_example2.jpg path_to_example2.xml
3. Run "./data/text/creat_data.sh"
#### Start training
1. modify the lmdb path in modelConfig.py
2. Run "python examples/text/train.py"