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OCR detection for ICDAR2015, which is based on FOTS, the precision is 80.6%.

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OCR detection for ICDAR2015, which is based on FOTS detection algorithm.


This project is a pytorch implementation of fots detection for OCR, and we focus on achieved the detection algorithm only:paper link-FOTS: Fast Oriented Text Spotting with a Unified Network. The code is created by Ning Lu originally, and we would like to appreaciate to his contributions.

What we are doing and going to do

  • [x] Change some code to make the project work.
  • [x] Add PSPnet model to experiment, but is not work effectively(the another project that we have doing: code).
  • [x] Support visdom.
  • [x] Support pytorch-0.4.1 or higher.


We benchmark our code thoroughly on the dataset: ICDAR2015, using network architecture: resnet50. It's worth noting that, the project had used the multi-scale to train network and haven't done the skill of OHEM. Below are the results:

1). ICDAR2015 (scale=512):

| model | #GPUs | batch size | lr | Recall | Precision | Hmean | | - | :-: | :-: | :-: | :-: | :-: | :-: | |Res-50 | 1080Ti | 4 | 1e-3 | 69.72% | 80.09% | 74.54% |


First of all, clone the code

git clone


  • Python 3.6
  • Pytorch 0.4.1
  • CUDA 8.0 or higher

Data Preparation

  • ICDAR 2015: Please download the dataset in the folder in your project named dataset, you can refer to any others. After downloading the data, creat softlinks in the folder data/.


Install all the python dependencies using pip:

pip install -r requirements.txt





If you want to evlauate the detection performance, simply run


Below are some detection results:


This project is equally contributed by Ning Lu and DongLiang Ma, and many others (thanks to them!).

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