TedEval: A Fair Evaluation Metric for Scene Text Detectors

Official Python 3 implementation of TedEval | paper | slides

Chae Young Lee, Youngmin Baek, and Hwalsuk Lee.

Clova AI Research, NAVER Corp.

Overview

We propose a new evaluation metric for scene text detectors called TedEval. Through separate instance-level matching policy and character-level scoring policy, TedEval solves the drawbacks of previous metrics such as IoU and DetEval. This code is based on ICDAR15 official evaluation code.

Methodology

1. Mathcing Policy

• Non-exclusively gathers all possible matches of not only one-to-one but also one-to-many and many-to-one.
• The threshold of both area recall and area precision are set to 0.4.
• Multiline is identified and rejected when |min(theta, 180 - theta)| > 45 from Fig. 2.

2. Scoring Policy

We compute Pseudo Character Center (PCC) from word-level bounding boxes and penalize matches when PCCs are missing or overlapping.

Sample Evaluation

Experiments

We evaluated state-of-the-art scene text detectors with TedEval on two benchmark datasets: ICDAR 2013 Focused Scene Text (IC13) and ICDAR 2015 Incidental Scene Text (IC15). Detectors are listed in the order of published dates.

ICDAR 2013

ICDAR 2015

Frequency

Getting Started

Clone repository

git clone https://github.com/clovaai/TedEval.git

Requirements

• python 3
• python 3.x Polygon, Bottle, Pillow

# install
pip3 install Polygon3 bottle Pillow

Supported Annotation Type

• LTRB(xmin, ymin, xmax, ymax)
• QUAD(x1, y1, x2, y2, x3, y3, x4, y4)

Evaluation

Prepare data

The ground truth and the result data should be text files, one for each sample. Note that the naming rule of each text file is that there must be img_{number} in the filename and that the number indicate the image sample.

# gt/gt_img_38.txt
644,101,932,113,932,168,643,156,concierge@L3
477,138,487,139,488,149,477,148,###
344,131,398,130,398,149,344,149,###
1195,148,1277,138,1277,177,1194,187,###
23,270,128,267,128,282,23,284,###

# result/res_img_38.txt
644,101,932,113,932,168,643,156,{Transcription},{Confidence}
477,138,487,139,488,149,477,148
344,131,398,130,398,149,344,149
1195,148,1277,138,1277,177,1194,187
23,270,128,267,128,282,23,284

Compress these text files.

zip gt.zip gt/*
zip result.zip result/*

Refer to gt/result.zip and gt/gt_*.zip for examples.

Run stand-alone evaluation

python script.py –g=gt/gt.zip –s=result/result.zip

• Locate the path of GT and submission file using the flag -g and -s, respectively.
• QUAD annotation type is used as default. To switch between {QUAD, LTRB}, add -p='{"LTRB" = False}' in the command or directly modify the default_evaluation_params() function in script.py.
• If there are transcription or confidence values in your submission file, add -p='{"CONFIDENCES" = True} or -p='{"TRANSCRIPTION" = True}'.

Run Visualizer

python web.py

• Place the zip file of images and GTs of the dataset named images.zip and gt.zip, respectively, in the gt directory.
• Create an empty directory name output. This is where the DB, submission files, and result files will be created.
• You can change the host and port number in the final line of web.py.

The file structure should then be:

.
├── gt
│   ├── gt.zip
│   └── images.zip
├── output   # empty dir
├── script.py
├── web.py
├── README.md
└── ...

Citation

@article{lee2019tedeval,
  title={TedEval: A Fair Evaluation Metric for Scene Text Detectors},
  author={Lee, Chae Young and Baek, Youngmin and Lee, Hwalsuk},
  journal={arXiv preprint arXiv:1907.01227},
  year={2019}
}

Contact us

We welcome any feedbacks to our metric. Please contact the authors via {cylee7133, youngmin.baek, hwalsuk.lee}@gmail.com. In case of code errors, open an issue and we will get to you.

License

Copyright (c) 2019-present NAVER Corp.

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