Scene Text-Spotting based on PSEnet+CRNN
Pytorch implementation of an end to end Text-Spotter with a PSEnet text detector and CRNN text recognizer. We plan to grow this repository into an open research platform for multi-lingual text detection and recognition from natural scene images, targeted towards low-resource languages.
Requirements
- Python 3.6.5
- Pytorch 1.2
- pyclipper
- Polygon 3.0.8
- OpenCV 3.4.1
Demo
- Download the trained CRNN and PSEnet models from the links provided below.
- Copy paths of the models and paste them in params.py
- run end-end.py
python end-end.py --img [path to image] --e2e_config_name [end to end config name]
Pre-trained Models
Both PSEnet and CRNN pre-trained models can be found here: gdrive
- the PSEnet model is a multi-lingual text detector, trained on MLT 2019. Works quite well!
- the CRNN recognizes Hindi, Bangla, Malayalam, Kanada, Tamil, Telugu, Odia, Sanskrit, Marathi!
Download the models in models/ directory and modify params.py if required.
Training instructions
- To train your own detection model refer to this file.
- To train your own recognition model refer to this file.
Samples
Contributors
- Azhar Shaikh, PES University LinkedIn
- Nishant Sinha, OffNote Labs
Work done as part of Internship with OffNote Labs.
References
If this repository helps you, please star it. Thank you!