Skip to content

Latest commit

 

History

History
96 lines (79 loc) · 4 KB

readme.md

File metadata and controls

96 lines (79 loc) · 4 KB
Raw

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:

  • Online demo
  • 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
  • Speed on 720p (resolution of 1280x720) images:
    • Now
      • Graphic card: GTX 1080 Ti
      • Network fprop: ~50 ms
      • NMS (C++): ~6ms
      • Overall: ~16 fps
    • Then
      • Graphic card: K40
      • Network fprop: ~150 ms
      • NMS (python): ~300ms
      • Overall: ~2 fps

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.

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=14 --checkpoint_path=/tmp/east_icdar2015_resnet_v1_50_rbox/ \
--text_scale=512 --training_data_path=/data/ocr/icdar2015/ --geometry=RBOX --learning_rate=0.0001 --num_readers=24 \
--pretrained_model_path=/tmp/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/

Demo

If you've downloaded the pre-trained model, you can setup a demo server by

python3 run_demo_server.py --checkpoint-path /tmp/east_icdar2015_resnet_v1_50_rbox/

Then open http://localhost:8769 for the web demo. Notice that the URL will change after you submitted an image. Something like ?r=49647854-7ac2-11e7-8bb7-80000210fe80 appends and that makes the URL persistent. As long as you are not deleting data in static/results, you can share your results to your friends using the same URL.

URL for example below: http://east.zxytim.com/?r=48e5020a-7b7f-11e7-b776-f23c91e0703e web-demo

Test

run

python eval.py --test_data_path=/tmp/images/ --gpu_list=0 --checkpoint_path=/tmp/east_icdar2015_resnet_v1_50_rbox/ \
--output_path=/tmp/

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

Examples

Here are some test examples on icdar2015, enjoy the beautiful text boxes! image_1 image_2 image_3 image_4 image_5

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