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CLEval: Character-Level Evaluation for Text Detection and Recognition Tasks

Official implementation of CLEval | paper

Overview

We propose a Character-Level Evaluation metric (CLEval). To perform fine-grained assessment of the results, instance matching process handles granularity difference and scoring process conducts character-level evaluation. Please refer to the paper for more details. This code is based on ICDAR15 official evaluation code.

Simplified Method Description

Explanation

Notification

  • 15 Jun, 2020 | initial release
    • the reported evaluation results in our paper is measured by setting the CASE_SENSITIVE option as False.

Supported annotation types

  • LTRB(xmin, ymin, xmax, ymax)
  • QUAD(x1, y1, x2, y2, x3, y3, x4, y4)
  • POLY(x1, y1, x2, y2, ..., x_2n, y_2n)

Supported datasets

  • ICDAR 2013 Focused Scene Text Link
  • ICDAR 2015 Incidental Scene Text Link
  • TotalText Link
  • Any other datasets that have a similar format with the datasets mentioned above

Getting started

Clone repository

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

Requirements

  • python 3.x
  • see requirements.txt file to check package dependency. To install, command
pip3 install -r requirements.txt

Instructions for the standalone scripts

Detection evaluation

python script.py -g=gt/gt_IC13.zip -s=[result.zip] --BOX_TYPE=LTRB          # IC13
python script.py -g=gt/gt_IC15.zip -s=[result.zip]                          # IC15
python script.py -g=gt/gt_TotalText.zip -s=[result.zip] --BOX_TYPE=POLY     # TotalText
  • Notes
    • The default value of BOX_TYPE is set to QUAD. It can be explicitly set to --BOX_TYPE=QUAD when running evaluation on IC15 dataset.
    • Add --TANSCRIPTION option if the result file contains transcription.
    • Add --CONFIDENCES option if the result file contains confidence.

End-to-end evaluation

python script.py -g=gt/gt_IC13.zip -s=[result.zip] --E2E --BOX_TYPE=LTRB        # IC13
python script.py -g=gt/gt_IC15.zip -s=[result.zip] --E2E                        # IC15
python script.py -g=gt/gt_TotalText.zip -s=[result.zip] --E2E --BOX_TYPE=POLY   # TotalText
  • Notes
    • Adding --E2E also automatically adds --TANSCRIPTION option. Make sure that the transcriptions are included in the result file.
    • Add --CONFIDENCES option if the result file contains confidence.

Paramter list

name type default description
--BOX_TYPE string QUAD annotation type of box (LTRB, QUAD, POLY)
--TRANSCRIPTION boolean False set True if result file has transcription
--CONFIDENCES boolean False set True if result file has confidence
--E2E boolean False to measure end-to-end evaluation (if not, detection evalution only)
--CASE_SENSITIVE boolean True set True to evaluate case-sensitively. (only used in end-to-end evaluation)
  • Note : Please refer to arg_parser.py file for additional parameters and default settings used internally.

Instructions for the webserver

Procedure

  1. Compress the GT file of the dataset you want to evaluate into gt.zip file and the image files into images.zip.
  2. Copy the two files to the ./gt/ directory.
  3. Run web.py with BOX_TYPE option.
python web.py --BOX_TYPE=[LTRB,QUAD,POLY] --PORT=8080

Paramters for webserver

name type default description
--BOX_TYPE string QUAD annotation type of box (LTRB, QUAD, POLY)
--PORT integer 8080 port number for web visualization

Web Server screenshots

TODO

  • Support to run the webserver with the designated GT and image files
  • Calculate the length of text based on grapheme for Mulit-lingual dataset

Citation

@article{baek2020cleval,
  title={CLEval: Character-Level Evaluation for Text Detection and Recognition Tasks},
  author={Youngmin Baek, Daehyun Nam, Sungrae Park, Junyeop Lee, Seung Shin, Jeonghun Baek, Chae Young Lee and Hwalsuk Lee},
  journal={arXiv preprint arXiv:2006.06244},
  year={2020}
}

Contact us

CLEval has been proposed to make fair evaluation in the OCR community, so we want to hear from many researchers. We welcome any feedbacks to our metric, and appreciate pull requests if you have any comments or improvements.

License

Copyright (c) 2020-present NAVER Corp.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.