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.

2023.10.16 Huge Update

• Much More Faster Version of CLEval has been Uploaded!!
• Support CLI
• Support torchmetric
• Support scale-wise evaluation

Simplified Method Description

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

Installation

Build from pip

download from Clova OCR pypi

$ pip install cleval

or build with url

$ pip install git+https://github.com/clovaai/CLEval.git --user

Build from source

$ git clone https://github.com/clovaai/CLEval.git
$ cd cleval
$ python setup.py install --user

How to use

You can replace cleval with PYTHONPATH=$PWD python cleval/main.py for evaluation using source.

$ PYTHONPATH=$PWD python cleval/main.py -g=gt/gt_IC13.zip -s=[result.zip] --BOX_TYPE=LTRB 

Detection evaluation (CLI)

$ cleval -g=gt/gt_IC13.zip -s=[result.zip] --BOX_TYPE=LTRB          # IC13
$ cleval -g=gt/gt_IC15.zip -s=[result.zip]                          # IC15
$ cleval -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 (CLI)

$ cleval -g=gt/gt_IC13.zip -s=[result.zip] --E2E --BOX_TYPE=LTRB        # IC13
$ cleval -g=gt/gt_IC15.zip -s=[result.zip] --E2E                        # IC15
$ cleval -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.

TorchMetric

from cleval import CLEvalMetric
metric = CLEvalMetric()

for gt, det in zip(gts, dets):
    # your fancy algorithm
    # ...
    # gt_quads = ...
    # det_quads = ...
    # ...
    _ = metric(det_quads, gt_quads, det_letters, gt_letters, gt_is_dcs)

metric_out = metric.compute()
metric.reset()

Profiling

$ cleval -g=resources/test_data/gt/gt_eval_doc_v1_kr_single.zip -s=resources/test_data/pred/res_eval_doc_v1_kr_single.zip --E2E -v --DEBUG --PPROFILE > profile.txt
$ PYTHONPATH=$PWD python cleval/main.py -g resources/test_data/gt/dummy_dataset_val.json -s resources/test_data/pred/dummy_dataset_val.json --SCALE_WISE --DOMAIN_WISE --ORIENTATION --E2E --ORIENTATION -v --PROFILE --DEBUG > profile.txt

Paramters for evaluation script

name	type	default	description
-g	string		path to ground truth zip file
-s	string		path to result zip file
-o	string		path to save per-sample result file 'results.zip'

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.

• Note : For scalewise evaluation, we measure the ratio of the shorter length (text height) of the text-box to the longer length of the image. Through this, evaluation for each ratio can be performed. To adjust the scales, please use SCALE_BINS argument.

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.

Contribute

Please use pre-commit which uses Black and Isort.

$ pip install pre-commit
$ pre-commit install

Step By Step

1. Write an issue.
2. Match code style (black, isort)
3. Wirte test code.
4. Delete branch after Squash&Merge.

Required Approve: 1

Code Maintainer

• Donghyun Kim ([email protected])