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https://github.com/liuheng92/tensorflow_PSENet
This is a tensorflow re-implementation of PSENet: Shape Robust Text Detection with Progressive Scale Expansion Network.My blog:
https://github.com/liuheng92/tensorflow_PSENet
cpp ocr psenet python tensorflow text-detection
Last synced: about 2 months ago
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This is a tensorflow re-implementation of PSENet: Shape Robust Text Detection with Progressive Scale Expansion Network.My blog:
- Host: GitHub
- URL: https://github.com/liuheng92/tensorflow_PSENet
- Owner: liuheng92
- License: mit
- Created: 2019-03-06T05:01:48.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-02-21T09:24:56.000Z (almost 5 years ago)
- Last Synced: 2024-08-02T11:15:32.855Z (5 months ago)
- Topics: cpp, ocr, psenet, python, tensorflow, text-detection
- Language: C++
- Homepage: https://blog.csdn.net/liuxiaoheng1992/article/details/87646951
- Size: 1.21 MB
- Stars: 491
- Watchers: 32
- Forks: 162
- Open Issues: 14
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
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README
# 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](https://arxiv.org/abs/1806.02559).
Thanks for the author's ([@whai362](https://github.com/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](https://pan.baidu.com/s/14tQHf9MjuD0lSmwkoZhnCg) extract code: pffd
[google drive](https://drive.google.com/file/d/1TjJvtwMp8hJXQhn6Yz2lbPdvBGH-ZQ8u/view?usp=sharing)
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
### 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. https://github.com/whai362/PSENet/issues/15
4. https://github.com/argman/EAST
### Acknowledge
[@rkshuai](https://github.com/rkshuai) found a bug about concat features in model.py.
**If this repository helps you，please star it. Thanks.**