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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

Original Image

After Text Detection

After Text Recognition

Contributors

Work done as part of Internship with OffNote Labs.

References

  1. https://github.com/whai362/PSENet
  2. https://github.com/Holmeyoung/crnn-pytorch

If this repository helps you, please star it. Thank you!

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Pytorch implementation of PSEnet with Pyramid Attention Network as feature extractor

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