IEICE TRANSACTIONS on Information
Synthetic Scene Character Generator and Ensemble Scheme with the Random Image Feature Method for Japanese and Chinese Scene Character Recognition
Summary :
Scene character recognition has been intensively investigated for a couple of decades because it has a great potential in many applications including automatic translation, signboard recognition, and reading assistance for the visually-impaired. However, scene characters are difficult to recognize at sufficient accuracy owing to various noise and image distortions. In addition, Japanese scene character recognition is more challenging and requires a large amount of character data for training because thousands of character classes exist in the language. Some researchers proposed training data augmentation techniques using Synthetic Scene Character Data (SSCD) to compensate for the shortage of training data. In this paper, we propose a Random Filter which is a new method for SSCD generation, and introduce an ensemble scheme with the Random Image Feature (RI-Feature) method. Since there has not been a large Japanese scene character dataset for the evaluation of the recognition systems, we have developed an open dataset JPSC1400, which consists of a large number of real Japanese scene characters. It is shown that the accuracy has been improved from 70.9% to 83.1% by introducing the RI-Feature method to the ensemble scheme.
- Publication
- IEICE TRANSACTIONS on Information Vol.E104-D No.11 pp.2002-2010
- Publication Date
- 2021/11/01
- Publicized
- 2021/08/24
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2021EDP7058
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
Authors
Fuma HORIE
Tohoku University
Hideaki GOTO
Tohoku University
Takuo SUGANUMA
Tohoku University
Keyword
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Fuma HORIE, Hideaki GOTO, Takuo SUGANUMA, "Synthetic Scene Character Generator and Ensemble Scheme with the Random Image Feature Method for Japanese and Chinese Scene Character Recognition" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 11, pp. 2002-2010, November 2021, doi: 10.1587/transinf.2021EDP7058.
Abstract: Scene character recognition has been intensively investigated for a couple of decades because it has a great potential in many applications including automatic translation, signboard recognition, and reading assistance for the visually-impaired. However, scene characters are difficult to recognize at sufficient accuracy owing to various noise and image distortions. In addition, Japanese scene character recognition is more challenging and requires a large amount of character data for training because thousands of character classes exist in the language. Some researchers proposed training data augmentation techniques using Synthetic Scene Character Data (SSCD) to compensate for the shortage of training data. In this paper, we propose a Random Filter which is a new method for SSCD generation, and introduce an ensemble scheme with the Random Image Feature (RI-Feature) method. Since there has not been a large Japanese scene character dataset for the evaluation of the recognition systems, we have developed an open dataset JPSC1400, which consists of a large number of real Japanese scene characters. It is shown that the accuracy has been improved from 70.9% to 83.1% by introducing the RI-Feature method to the ensemble scheme.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7058/_p
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@ARTICLE{e104-d_11_2002,
author={Fuma HORIE, Hideaki GOTO, Takuo SUGANUMA, },
journal={IEICE TRANSACTIONS on Information},
title={Synthetic Scene Character Generator and Ensemble Scheme with the Random Image Feature Method for Japanese and Chinese Scene Character Recognition},
year={2021},
volume={E104-D},
number={11},
pages={2002-2010},
abstract={Scene character recognition has been intensively investigated for a couple of decades because it has a great potential in many applications including automatic translation, signboard recognition, and reading assistance for the visually-impaired. However, scene characters are difficult to recognize at sufficient accuracy owing to various noise and image distortions. In addition, Japanese scene character recognition is more challenging and requires a large amount of character data for training because thousands of character classes exist in the language. Some researchers proposed training data augmentation techniques using Synthetic Scene Character Data (SSCD) to compensate for the shortage of training data. In this paper, we propose a Random Filter which is a new method for SSCD generation, and introduce an ensemble scheme with the Random Image Feature (RI-Feature) method. Since there has not been a large Japanese scene character dataset for the evaluation of the recognition systems, we have developed an open dataset JPSC1400, which consists of a large number of real Japanese scene characters. It is shown that the accuracy has been improved from 70.9% to 83.1% by introducing the RI-Feature method to the ensemble scheme.},
keywords={},
doi={10.1587/transinf.2021EDP7058},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - Synthetic Scene Character Generator and Ensemble Scheme with the Random Image Feature Method for Japanese and Chinese Scene Character Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 2002
EP - 2010
AU - Fuma HORIE
AU - Hideaki GOTO
AU - Takuo SUGANUMA
PY - 2021
DO - 10.1587/transinf.2021EDP7058
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2021
AB - Scene character recognition has been intensively investigated for a couple of decades because it has a great potential in many applications including automatic translation, signboard recognition, and reading assistance for the visually-impaired. However, scene characters are difficult to recognize at sufficient accuracy owing to various noise and image distortions. In addition, Japanese scene character recognition is more challenging and requires a large amount of character data for training because thousands of character classes exist in the language. Some researchers proposed training data augmentation techniques using Synthetic Scene Character Data (SSCD) to compensate for the shortage of training data. In this paper, we propose a Random Filter which is a new method for SSCD generation, and introduce an ensemble scheme with the Random Image Feature (RI-Feature) method. Since there has not been a large Japanese scene character dataset for the evaluation of the recognition systems, we have developed an open dataset JPSC1400, which consists of a large number of real Japanese scene characters. It is shown that the accuracy has been improved from 70.9% to 83.1% by introducing the RI-Feature method to the ensemble scheme.
ER -
English
