STD++: A Semantic-based Arbitrarily-Oriented Scene Text Detector

This repository is the official implementation of A Semantic-based Arbitrarily-Oriented Scene Text Detector(named STD++ as it is the improved version of STD).due to lack of computing resources and time, we tested STD++ on MTWI2018 dataset, and we hope to perform more experiments on any other benchmark datasets, such as IC15,IC17,COCO-Text,MSRA-TD500 and so on.

images come from icdar2017rctw

Introduction

STD++ is the improved version of STD, which solved STD's limitations and can be used to detect arbitrarily-oriented texts, yet still preserves its accuracy and efficiency:

• no any further post-processings, like NMS.
• anchor-free.
• easy to generate training labels.
• only one step process to get final bounding boxes.

Any questions or suggestions,please drop a comment or contact me,email: gao.gzhou@gmail.com.

Training

Download RCTW17 dataset below, and configure your local directory path. refer to train.py

Inference

predict.py

Examples

Dataset

We trained STD++ on MTWI2018 dataset, training and testing images can be downloaded from this site for Text Localization, and we make STD++ annotations available on baiduyun, code: nuti.

License

This project is released under the Apache 2.0 license.

Citation

If you use our codebase in your research, please cite this project. a paper or technical report will be released soon.

And besides, you are welcomed to join us to maintain this project.

@misc{std_plus_plus2019,
  author =       {Gao Lijun},
  title =        {STD++: A Semantic-based Arbitrarily-Oriented Scene Text Detector},
  howpublished = {\url{https://github.com/opconty/keras_std_plus_plus}},
  year =         {2019}
}