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https://github.com/lvpengyuan/masktextspotter.caffe2
The code of "Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes"
https://github.com/lvpengyuan/masktextspotter.caffe2
Last synced: 10 days ago
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The code of "Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes"
- Host: GitHub
- URL: https://github.com/lvpengyuan/masktextspotter.caffe2
- Owner: lvpengyuan
- License: apache-2.0
- Created: 2018-01-25T08:10:49.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2019-10-09T05:20:43.000Z (about 5 years ago)
- Last Synced: 2024-08-02T11:14:54.397Z (3 months ago)
- Language: Python
- Homepage:
- Size: 3.87 MB
- Stars: 261
- Watchers: 20
- Forks: 88
- Open Issues: 33
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Mask TextSpotter
### A Pytorch implementation of Mask TextSpotter along with its extension can be find [here](https://github.com/MhLiao/MaskTextSpotter)
### Introduction
This is the official implementation of Mask TextSpotter.
Mask TextSpotter is an End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes
For more details, please refer to our [paper](https://arxiv.org/abs/1807.02242).
### Citing the paper
Please cite the paper in your publications if it helps your research:
@inproceedings{LyuLYWB18,
author = {Pengyuan Lyu and
Minghui Liao and
Cong Yao and
Wenhao Wu and
Xiang Bai},
title = {Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes},
booktitle = {Proc. ECCV},
pages = {71--88},
year = {2018}
}
### Contents
1. [Requirements](#requirements)
2. [Installation](#installation)
3. [Models](#models)
4. [Datasets](#datasets)
5. [Test](#test)
6. [Train](#train)
### Requirements
- NVIDIA GPU, Linux, Python2
- Caffe2, various standard Python packages
### Installation
#### Caffe2
To install Caffe2 with CUDA support, follow the [installation instructions](https://caffe2.ai/docs/getting-started.html) from the [Caffe2 website](https://caffe2.ai/). **If you already have Caffe2 installed, make sure to update your Caffe2 to a version that includes the [Detectron module](https://github.com/caffe2/caffe2/tree/master/modules/detectron).**
Please ensure that your Caffe2 installation was successful before proceeding by running the following commands and checking their output as directed in the comments.
```
# To check if Caffe2 build was successful
python2 -c 'from caffe2.python import core' 2>/dev/null && echo "Success" || echo "Failure"
# To check if Caffe2 GPU build was successful
# This must print a number > 0 in order to use Detectron
python2 -c 'from caffe2.python import workspace; print(workspace.NumCudaDevices())'
```
If the `caffe2` Python package is not found, you likely need to adjust your `PYTHONPATH` environment variable to include its location (`/path/to/caffe2/build`, where `build` is the Caffe2 CMake build directory).
Install Python dependencies:
```
pip install numpy pyyaml matplotlib opencv-python>=3.0 setuptools Cython mock
```
Set up Python modules:
```
cd $ROOT_DIR/lib && make
```
Note: Caffe2 is difficult to install sometimes.
### Models
Download the model and place it as ```models/model_iter79999.pkl```
Our trained model:
[Google Drive](https://drive.google.com/open?id=1yPATzUCREBopDIHcsvdYOBB3YpStunMU);
[BaiduYun](https://pan.baidu.com/s/1JPZmOQ1LAw98s0GPa-PuuQ) (key of BaiduYun: gnpc)
### Datasets
Download the ICDAR2013([Google Drive](https://drive.google.com/open?id=1sptDnAomQHFVZbjvnWt2uBvyeJ-gEl-A), [BaiduYun](https://pan.baidu.com/s/18W2aFe_qOH8YQUDg4OMZdw)) and ICDAR2015([Google Drive](https://drive.google.com/open?id=1HZ4Pbx6TM9cXO3gDyV04A4Gn9fTf2b5X), [BaiduYun](https://pan.baidu.com/s/16GzPPzC5kXpdgOB_76A3cA)) as examples.
Datasets should be placed in ```lib/datasets/data/``` as below
```
synth
icdar2013
icdar2015
scut-eng-char
totaltext
```
If you do not train the model, you can just download the ICDAR2013 or ICDAR2015 datasets for testing.
### Test
```
python tools/test_net.py --cfg configs/text/mask_textspotter.yaml
```
You can modify the model path or the test dataset in ```configs/text/mask_textspotter.yaml```.
### Train
You should format all the datasets you used for training as above.
Then modify ```configs/text/mask_textspotter.yaml``` to fit the gpus, model path, and datasets.
```
python tools/train_net.py --cfg configs/text/mask_textspotter.yaml
```