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RF-Learning

1. Introduction

This code repository contains an implementation of (RF-Learning: Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition (ICDAR 2021)) . We excavate the implicit task, character counting within the traditional text recognition, without additional labor annotation cost. The implicit task plays as an auxiliary branch for complementing the sequential recognition. We design a two-branch reciprocal feature learning framework in order to adequately utilize the features from both the tasks. Through exploiting the complementary effect between explicit and implicit tasks, the feature is reliably enhanced.

2. Preparing Dataset

Train Dataset

Dataset Samples Description Release
MJSynth 8919257 Scene text recognition synthetic data set Link
SynText 7266164 A synthesized by scene text dataset, and the text is cropped from the large image Link

Validation Dataset

Test Set Instance Number Note
IIIT5K 3000 regular
SVT 647 regular
IC03_860 860 regular
IC13_857 857 regular
IC15_1811 1811 irregular
SVTP 645 irregular
CUTE80 288 irregular

Test Dataset

Test Set Instance Number Note
IIIT5K 3000 regular
SVT 647 regular
IC03_860 860 regular
IC13_857 857 regular
IC15_1811 1811 irregular
SVTP 645 irregular
CUTE80 288 irregular

3. Getting Started

Preparation

A quick start is to use above lmdb-formatted datasets that contain the full benchmarks for scene text recognition tasks as belows.

Data Type: LMDB

File storage format:
   |-- train           
   |   |-- MJ
   |   |-- ST
   |-- validation
   |   |-- mixture
   |-- evaluation
   |   |-- mixture

Training

Run the following bash command in the command line,

cd .
bash ./train.sh 

We provide the implementation of online validation. If you want to close it to save training time, you may modify the startup script to add --no-validate command.

Evaluation

1.Visual Stage

  cd .
  bash ./test_scripts/test_rfl_visual.sh

2.Total Stage

  cd .
  bash ./test_scripts/test_rfl_total.sh

4. Results

Evaluation

Methods Regular Text Irregular Text Download
Name IIIT5K SVT IC03 IC13 IC15 SVTP CUTE80 Config Model
RF-Learning visual(Report) 95.7 94.0 96.0 95.2 84.2 87.0 85.8

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RF-Learning visual 96.0 94.7 96.2 95.9 88.7 86.7 88.2

Config

pth BaiduYunPan (Code:opjk), Google Drive

RF-Learning total(Report) 94.1 88.6 94.9 94.5 82.4 82.0 82.6

-

-

RF-Learning total 94.5 90.0 94.0 94.1 81.5 82.0 84.7

Config

pth BaiduYunPan (Code:opjk), Google Drive

Visualization

Here is the picture for result visualization.

visualization

Citation

@article{rflearning,
  author={Hui Jiang and Yunlu Xu and Zhanzhan Cheng and Shiliang Pu and Yi Niu and Wenqi Ren and Fei Wu and Wenming Tan},
  title={Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition},
  journal={CoRR},
  volume={abs/2105.06229},
  year={2021},
}

License

This project is released under the Apache 2.0 license

Copyright

If there is any suggestion and problem, please feel free to contact the author with jianghui11@hikvision.com or chengzhanzhan@hikvision.com.