EAST文本检测

# EAST: An Efficient and Accurate Scene Text Detector ### Introduction This is a tensorflow re-implementation of [EAST: An Efficient and Accurate Scene Text Detector](https://arxiv.org/abs/1704.03155v2). The features are summarized blow: + Online demo + http://east.zxytim.com/ + Result example: http://east.zxytim.com/?r=48e5020a-7b7f-11e7-b776-f23c91e0703e + CAVEAT: There's only one cpu core on the demo server. Simultaneous access will degrade response time. + 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](http://rrc.cvc.uab.es/?ch=4&com=evaluation&view=method_samples&task=1&m=29855&gtv=1) 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](https://github.com/zxytim)) help! Please cite his [paper](https://arxiv.org/abs/1704.03155v2) if you find this useful. ### Contents 1. [Installation](#installation) 2. [Download](#download) 2. [Demo](#demo) 3. [Test](#train) 4. [Train](#test) 5. [Examples](#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](http://pan.baidu.com/s/1jHWDrYQ) [GoogleDrive](https://drive.google.com/open?id=0B3APw5BZJ67ETHNPaU9xUkVoV0U) 2. Resnet V1 50 provided by tensorflow slim: [slim resnet v1 50](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz) ### 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  ### 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!      Please let me know if you encounter any issues(my email boostczc@gmail dot com).