EAST_ICPR: EAST for ICPR MTWI 2018 CHALLENGE

Introduction

This is a repository forked from argman/EAST for the ICPR MTWI 2018 CHALLENGE.
Origin Repository: argman/EAST - A tensorflow implementation of EAST text detector
Origin Author: argman

Author: Haozheng Li
Email: sai-2008@qq.com or akaHaozhengLi@gmail.com

Transform
Models
Demo
Train
Test
Results

Transform

Some data in the dataset is abnormal, just like ICPR MTWI 2018. Abnormal means that the ground true labels are anticlockwise, or the images are not in 3 channels. Then errors like 'poly in wrong direction' will occur while using argman/EAST.

So I wrote a matlab program to check and transform the dataset. The program named <transform.m> is in the folder 'data_transform/' and its parameters are descripted as bellow:

icpr_img_folder = 'image_9000\';                   %origin images
icpr_txt_folder = 'txt_9000\';                     %origin ground true labels
icdar_img_folder = 'ICPR2018\';                    %transformed images
icdar_gt_folder = 'ICPR2018\';                     %transformed ground true labels
icdar_img_abnormal_folder = 'ICPR2018_abnormal\';  %images not in 3 channels, which give errors in argman/EAST
icdar_gt_abnormal_folder = 'ICPR2018_abnormal\';   %transformed ground true labels

%images and ground true labels files must be renamed as <img_1>, <img_2>, ..., <img_xxx> while using argman/EAST
first_index =  0;                                  %first index of the dataset
transform_list_name = 'transform_list.txt';        %file name of the rename list

Note: For abnormal images not in 3 channels, please transform them to normal ones through other tools like Format Factory. Then add the right data to the <icdar_img_folder> and <icdar_gt_folder>, so finally you get a whole normal dataset which has been checked and transformed.

Models

Resnet_V1_50 Models trained on ICPR MTWI 2018 (train): [100k steps] [500k steps] [1035k steps]
Resnet_V1_101 Models trained on ICPR MTWI 2018 (train) + ICDAR 2017 MLT (train + val) + RCTW-17 (train): [100k steps]
Resnet_V1_101 Models pre-trained on Models-2, then trained on just ICPR MTWI 2018 (train): [987k steps]
(In argman/EAST) Resnet_V1_50 Models trained on ICDAR 2013 (train) + ICDAR 2015 (train): [50k steps]
(In argman/EAST) Resnet_V1_50 Models provided by tensorflow slim: [slim_resnet_v1_50]

Demo

Download the pre-trained models and run:

python run_demo_server.py --checkpoint-path models/east_icpr2018_resnet_v1_50_rbox_1035k/

Then Open http://localhost:8769 for the web demo server, or get the results in 'static/results/'.
Note: See argman/EAST#demo for more details.

Train

Prepare the training set and run:

python multigpu_train.py --gpu_list=0 --input_size=512 --batch_size_per_gpu=8 \
--checkpoint_path=models/east_icpr2018_resnet_v1_50_rbox/ \
--text_scale=512 --training_data_path=data/ICPR2018/ --geometry=RBOX \
--learning_rate=0.0001 --num_readers=18 --max_steps=50000

Note 1: Images and ground true labels files must be renamed as <img_1>, <img_2>, ..., <img_xxx> while using argman/EAST. Please see the examples in the folder 'training_samples/'.
Note 2: If --restore=True, training will restore from checkpoint and ignore the --pretrained_model_path. If --restore=False, training will delete checkpoint and initialize with the --pretrained_model_path (if exists).
Note 3: See argman/EAST#train for more details.

Test

Names of the images in ICPR MTWI 2018 are abnormal. Like <LB1gXi2JVXXXXXUXFXXXXXXXXXX.jpg> but not <img_10001.jpg>. Then errors will occur while using argman/EAST#test.

So I wrote two matlab programs to rename and inversely rename the dataset. Before evaluating, run the program named <rename.m> to make names of the images normal. This program is in the folder 'data_transform/' and its parameters are descripted as bellow:

icpr_img_folder = 'image_10000\';                      %origin images
icdar_img_folder = 'ICPR2018_test\';                   %transformed images
icdar_img_abnormal_folder = 'ICPR2018_test_abnormal\'; %images not in 3 channels, which give errors in argman/EAST

icpr_count =  10000;                                   %first index of the dataset
rename_list_name = 'rename_list.txt';                  %file name of the rename list

Note: Just like <transform.m>, please transform abnormal images through other tools like Format Factory.

After you have prepared the test set, run:

python eval.py --test_data_path=data/ICPR2018/ --gpu_list=0 \
--checkpoint_path=models/east_icpr2018_resnet_v1_50_rbox_1035k/ --output_dir=results/1035k/

Then get the results in 'results/'.

Finally inversely rename the result labels files from <img_10001.txt> to <LB1gXi2JVXXXXXUXFXXXXXXXXXX.txt> according to the rename list generated by <rename.m>. Run the program named <rename_inverse.m> which is in the folder 'data_transform/' and its parameters are descripted as bellow:

rename_list_name = 'rename_list.txt';  %file name of the rename list
icpr_img_folder = 'image_10000\';      %origin images
icpr_txt_folder = 'results\';          %result labels files generated by argman/EAST
icdar_gt_folder = 'txt_10000\';        %inversely renamed result labels files

Then zip the results in 'txt_10000/' and submit it to the ICPR MTWI 2018 CHALLENGE.

Results

Finally our model <east_icpr2018_resnet_v1_50_rbox_1035k> rank 31 in the ICPR MTWI 2018 CHALLENGE:

Here are some results on ICPR MTWI 2018:

leichangqing/EAST_ICPR