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Mask TextSpotter

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.

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
  2. Installation
  3. Models
  4. Datasets
  5. Test
  6. Train

Requirements

  • NVIDIA GPU, Linux, Python2
  • Caffe2, various standard Python packages

Installation

Caffe2

To install Caffe2 with CUDA support, follow the installation instructions from the Caffe2 website. If you already have Caffe2 installed, make sure to update your Caffe2 to a version that includes the Detectron module.

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; BaiduYun (key of BaiduYun: gnpc)

Datasets

Download the ICDAR2013(Google Drive, BaiduYun) and ICDAR2015(Google Drive, BaiduYun) 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