/tensorflow_PSENet

This is a tensorflow re-implementation of PSENet: Shape Robust Text Detection with Progressive Scale Expansion Network.My blog:

Primary LanguageC++

PSENet: Shape Robust Text Detection with Progressive Scale Expansion Network

Introduction

This is a tensorflow re-implementation of PSENet: Shape Robust Text Detection with Progressive Scale Expansion Network.

Thanks for the author's (@whai362) awesome work!

Installation

  1. Any version of tensorflow version > 1.0 should be ok.
  2. python 2 or 3 will be ok.

Download

trained on ICDAR 2015 (training set) + ICDAR2017 MLT (training set):

baiduyun extract code: pffd

google drive

This model is not as good as article's, it's just a reference. You can finetune on it or you can do a lot of optimization based on this code.

Database Precision (%) Recall (%) F-measure (%)
ICDAR 2015(val) 74.61 80.93 77.64

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 make sure that gt text and image file have the same names.

Then run train.py like:

python train.py --gpu_list=0 --input_size=512 --batch_size_per_gpu=8 --checkpoint_path=./resnet_v1_50/ \
--training_data_path=./data/ocr/icdar2015/

If you have more than one gpu, you can pass gpu ids to gpu_list(like --gpu_list=0,1,2,3)

Note:

  1. right now , only support icdar2017 data format input, like (116,1179,206,1179,206,1207,116,1207,"###"), but you can modify data_provider.py to support polygon format input
  2. Already support polygon shrink by using pyclipper module
  3. this re-implementation is just for fun, but I'll continue to improve this code.
  4. re-implementation pse algorithm by using c++ (if you use python2, just run it, if python3, please replace python-config with python3-config in makefile)

Test

run eval.py like:

python eval.py --test_data_path=./tmp/images/ --gpu_list=0 --checkpoint_path=./resnet_v1_50/ \
--output_dir=./tmp/

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

Examples

result0 result1 result2 result3 result4 result5

About issues

If you encounter any issue check issues first, or you can open a new issue.

Reference

  1. http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz
  2. https://github.com/CharlesShang/FastMaskRCNN
  3. whai362/PSENet#15
  4. https://github.com/argman/EAST

Acknowledge

@rkshuai found a bug about concat features in model.py.

If this repository helps you，please star it. Thanks.