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NRTR

Introduction

[ALGORITHM]

@inproceedings{sheng2019nrtr,
  title={NRTR: A no-recurrence sequence-to-sequence model for scene text recognition},
  author={Sheng, Fenfen and Chen, Zhineng and Xu, Bo},
  booktitle={2019 International Conference on Document Analysis and Recognition (ICDAR)},
  pages={781--786},
  year={2019},
  organization={IEEE}
}

[BACKBONE]

@inproceedings{li2019show,
  title={Show, attend and read: A simple and strong baseline for irregular text recognition},
  author={Li, Hui and Wang, Peng and Shen, Chunhua and Zhang, Guyu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={33},
  number={01},
  pages={8610--8617},
  year={2019}
}

Dataset

Train Dataset

trainset instance_num repeat_num source
SynthText 7266686 1 synth
Syn90k 8919273 1 synth

Test Dataset

testset instance_num type
IIIT5K 3000 regular
SVT 647 regular
IC13 1015 regular
IC15 2077 irregular
SVTP 645 irregular
CT80 288 irregular

Results and Models

Methods Backbone Regular Text Irregular Text download
IIIT5K SVT IC13 IC15 SVTP CT80
NRTR R31-1/16-1/8 93.9 90.0 93.5 74.5 78.5 86.5 model | log
NRTR R31-1/8-1/4 94.7 87.5 93.3 75.1 78.9 87.9 model | log

Notes:

  • R31-1/16-1/8 means the height of feature from backbone is 1/16 of input image, where 1/8 for width.
  • R31-1/8-1/4 means the height of feature from backbone is 1/8 of input image, where 1/4 for width.