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

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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:

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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