EAST: An Efficient and Accurate Scene Text Detector

Introduction

This is a tensorflow re-implementation of EAST: An Efficient and Accurate Scene Text Detector. The features are summarized blow:

• Only RBOX part is implemented.
• A fast Locality-Aware NMS in C++ provided by the paper's author.
• The pre-trained model provided achieves 80.83 F1-score on ICDAR 2015 Incidental Scene Text Detection Challenge using only training images from ICDAR 2015 and 2013. see here for the detailed results.
• Differences from original paper
  • Use ResNet-50 rather than PVANET
  • Use dice loss (optimize IoU of segmentation) rather than balanced cross entropy
  • Use linear learning rate decay rather than staged learning rate decay Thanks for the author's (@zxytim) help! Please cite his paper if you find this useful.

Contents

1. Installation
2. Download
3. Demo
4. Test
5. Train
6. Examples

Installation

1. Any version of tensorflow version > 1.0 should be ok.
2. sudo apt-get install python3-tk

Download

1. Models trained on ICDAR 2013 (training set) + ICDAR 2015 (training set): BaiduYun link GoogleDrive
2. Resnet V1 50 provided by tensorflow slim: slim resnet v1 50

Train

If you want to train the model, you should provide the dataset path, in the dataset path, a separate gt text file should be provided for each image and run

python multigpu_train.py --gpu_list=0 --input_size=512 --batch_size_per_gpu=8 --checkpoint_path=/content/EAST/tmp/east_icdar2015_resnet_v1_50_rbox/ --text_scale=512 --training_data_path=/content/EAST/data/sroie_train/ --geometry=RBOX --learning_rate=0.0001 --num_readers=4 --pretrained_model_path=/content/EAST/data/resnet_v1_50.ckpt

If you have more than one gpu, you can pass gpu ids to gpu_list(like --gpu_list=0,1,2,3)

Note: you should change the gt text file of icdar2015's filename to img_*.txt instead of gt_img_*.txt(or you can change the code in icdar.py), and some extra characters should be removed from the file. See the examples in training_samples/

This executable line is updated with reduced num_readers and batch_size_per_gpu, and code is running on top of pre-trained checkpoints. Update current checkpoint file path in 'checkpoint' file in east_icdar2015_resnet_v1_50_rbox folder

Test

run

python eval.py --test_data_path=/content/EAST/tmp/images/ --gpu_list=0 --checkpoint_path=/content/EAST/east_icdar2015_resnet_v1_50_rbox/  --output_dir=/content/EAST/tmp/output/

a text file will be then written to the output path.

Test Execution on Windows/CPU environment

Open the x64 or x32 Visual Studio developer command prompt (or Native Tools Command Prompt) in Windows 10 and use the following command to generate the adaptor.pyd file: first cd into lanms folder to execute below command in command prompt,

cl adaptor.cpp ./include/clipper/clipper.cpp /I ./include /I "C:\Python36\include" /LD /Fe:adaptor.pyd /link/LIBPATH:"C:\Python36\libs"

This will generate required files for Windows execution/testing

Note please make sure you comment some code in init.py in lanms folder comment line 7 and 8

if subprocess.call(['make', '-C', BASE_DIR]) != 0:  # return value
    raise RuntimeError('Cannot compile lanms: {}'.format(BASE_DIR))

Examples

Here are some test examples on icdar2015, enjoy the beautiful text boxes!

Troubleshooting

• How to compile lanms on Windows ?
  • See argman#120

Please let me know if you encounter any issues(my email boostczc@gmail dot com).