Fast Oriented Text Spotting with a Unified Networkt

Update

[08/17/2019] A new version is updated, please checkout the branch 'dev' (link).

Introduction

This is an implementation of FOTS: Fast Oriented Text Spotting with a Unified Network

Install

• Python2
• tensorflow
• OpenCV

  Model

  Model pretrained on Synth800 for 6 epoch and finetuned on ICDAR15 BaiduYunLink keys:0aky or GithubLink thanks for harish2704. If you encounter problems, you can refer to #16.

  Train

  python2 multigpu_train.py --gpu_list=gpu_id --training_data_path=/path/to/trainset/

You should also change line 824 in icdar.py should be changed for the path of annotation file

Test

python2 eval.py --gpu_list=gpu_id --test_data_path=/path/to/testset/ --checkpoint_path=checkpoints/

Examples

Differences from paper

• Without OHEM
• Pretrained on Synth800k for 6 epochs not 10 epochs
• Fine-tuned on ICDAR15 only without ICDAR2017 MLT
• And it can only get F-score 56 on ICDAR2015 testset, more training tricks are needed

  Reference

• EAST
• FOTS.Pytorch Thanks for the authors!