OCR detection for ICDAR2015, which is based on FOTS, the precision is 80.6%.
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.
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 https://github.com/Vipermdl/OCR_detection_IC15
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: