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Oct 18, 2018
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E2E-MLT

E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text code base for: https://arxiv.org/abs/1801.09919

@@inproceedings{buvsta2018e2e,
  title={E2E-MLT-an unconstrained end-to-end method for multi-language scene text},
  author={Bu{\v{s}}ta, Michal and Patel, Yash and Matas, Jiri},
  booktitle={Asian Conference on Computer Vision},
  pages={127--143},
  year={2018},
  organization={Springer}
}

Requirements

Pretrained Models

e2e-mlt, e2e-mlt-rctw

wget http://ptak.felk.cvut.cz/public_datasets/SyntText/e2e-mlt.h5

Running Demo

python3 demo.py -model=e2e-mlt.h5

Data

MLT SynthSet

Synthetic text has been generated using Synthetic Data for Text Localisation in Natural Images, with minor changes for Arabic and Bangla script rendering.

What we have found useful:

  • for generating Arabic Scene Text: https://github.com/mpcabd/python-arabic-reshaper
  • for generating Bangla Scene Text: PyQt4
  • having somebody who can read non-latin scripts: we would like to thank Ali Anas for reviewing generated Arabic scene text.

Training

python3 train.py -train_list=sample_train_data/MLT/trainMLT.txt -batch_size=8 -num_readers=5 -debug=0 -input_size=512 -ocr_batch_size=256 -ocr_feed_list=sample_train_data/MLT_CROPS/gt.txt

Acknowledgments

Code borrows from EAST and DeepTextSpotter

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E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text

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