I am still working on this repo. updates and detailed instructions are coming soon!

• TensorFlow Versions
• Other Requirements
• Trained Models
• Datasets
• Train
  • Pre-train with SynthText
  • Finetune with ICDAR 2015, ICDAR 2017 MLT or ICDAR 2013
• Test
• References

As for now, the pre-training code is tested on TensorFlow 1.12, 1.14 and 1.15. I may try to implement 2.x version in the future.

GCC >= 6

• tmp pre-trained model
• trained model comming soon

• pre-training
  Synth800k(The dataset is only available for non-commercial research and educational purposes)
• finetuning
  ICDAR 2015, 2017MLT, 2013

1. Download pre-trained ResNet-50 from TensorFlow-Slim image classification model library page and place it at 'ckpt/resnet_v1_50' dir.
   

cd ckpt/resnet_v1_50
wget http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz
tar -zxvf resnet_v1_50_2016_08_28.tar.gz
rm resnet_v1_50_2016_08_28.tar.gz

2. Download Synth800k dataset and place it at data/SynthText/ dir to pre-train the whole net.
   

3. Transform(Pre-process) the SynthText data into the ICDAR data format.
   

python data_provider/SynthText2ICDAR.py

4. Train with SynthText for 10 epochs(with 1 GPU).
   

python train.py \
  --max_steps=715625 \
  --gpu_list='0' \
  --checkpoint_path=ckpt/synthText_10eps/ \
  --pretrained_model_path=ckpt/resnet_v1_50/resnet_v1_50.ckpt \
  --training_img_data_dir=data/SynthText/ \
  --training_gt_data_dir=data/SynthText/ \
  --icdar=False \

5. Visualize pre-pretraining progress with TensorBoard.

tensorboard --logdir=ckpt/synthText_10eps/

(if you are using the pre-trained model, place all of the files in ckpt/synthText_10eps/)

• Combine ICDAR data before training.

  1. Place ICDAR data under tmp/ foler.
  2. Run the following script to combine the data.

  python combine_ICDAR_data.py --year [year of ICDAR to train(13 or 15 or 17)]
  

• ICDAR 2017 MLT/pre-finetune for ICDAR 2013 or ICDAR 2015 (text detection task only)

  • Train the pre-trained model with 9,000 images from ICDAR 2017 MLT training and validation datasets(with 1 GPU).

  python train.py \
    --gpu_list='0' \
    --checkpoint_path=ckpt/ICDAR17MLT/ \
    --pretrained_model_path=ckpt/synthText_10eps/ \
    --train_stage=0 \
    --training_img_data_dir=data/ICDAR17MLT/imgs/ \
    --training_gt_data_dir=data/ICDAR17MLT/gts/
  

• ICDAR 2015

  • Train the model with 1,000 images from ICDAR 2015 training dataset and 229 images from ICDAR 2013 training datasets(with 1 GPU).

  python train.py \
    --gpu_list='0' \
    --checkpoint_path=ckpt/ICDAR15/ \
    --pretrained_model_path=ckpt/ICDAR17MLT/ \
    --training_img_data_dir=data/ICDAR15+13/imgs/ \
    --training_gt_data_dir=data/ICDAR15+13/gts/
  

• ICDAR 2013(horizontal text only)

  • Train the model with 229 images from ICDAR 2013 training datasets(with 1 GPU).

  python train.py \
    --gpu_list='0' \
    --checkpoint_path=ckpt/ICDAR13/ \
    --pretrained_model_path=ckpt/ICDAR17MLT/ \
    --training_img_data_dir=data/ICDAR13/imgs/ \
    --training_gt_data_dir=data/ICDAR13/gts/
  

Place some images in test_imgs/ dir and specify a trained checkpoint path to see the test result.

python test.py --test_data_path test_imgs/ --checkpoint_path [checkpoint path]

• Paper
  • FOTS: Fast Oriented Text Spotting with a Unified Network
    
  • Deep Residual Learning for Image Recognition
    
  • Feature Pyramid Networks for Object Detection
    
• Repos
  • https://github.com/yu20103983/FOTS
    
  • https://github.com/Pay20Y/FOTS_TF/tree/dev
    
  • https://github.com/tensorflow/models/tree/master/research/slim
    
  • https://github.com/kaiminghe/deep-residual-networks
    
  • https://github.com/Parquery/lanms