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}
}
wget http://ptak.felk.cvut.cz/public_datasets/SyntText/e2e-mlt.h5
python3 demo.py -model=e2e-mlt.h5
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:
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
Code borrows from EAST and DeepTextSpotter