CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Semi-Supervised Scene Text Recognition
Gao, Yunze1,2; Chen, Yingying1,2; Wang, Jinqiao1,2; Lu, Hanqing1,2
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
2021
Volume30Pages:3005-3016
Corresponding AuthorChen, Yingying(yingying.chen@nlpr.ia.ac.cn)
AbstractScene text recognition has been widely researched with supervised approaches. Most existing algorithms require a large amount of labeled data and some methods even require character-level or pixel-wise supervision information. However, labeled data is expensive, unlabeled data is relatively easy to collect, especially for many languages with fewer resources. In this paper, we propose a novel semi-supervised method for scene text recognition. Specifically, we design two global metrics, i.e., edit reward and embedding reward, to evaluate the quality of generated string and adopt reinforcement learning techniques to directly optimize these rewards. The edit reward measures the distance between the ground truth label and the generated string. Besides, the image feature and string feature are embedded into a common space and the embedding reward is defined by the similarity between the input image and generated string. It is natural that the generated string should be the nearest with the image it is generated from. Therefore, the embedding reward can be obtained without any ground truth information. In this way, we can effectively exploit a large number of unlabeled images to improve the recognition performance without any additional laborious annotations. Extensive experimental evaluations on the five challenging benchmarks, the Street View Text, IIIT5K, and ICDAR datasets demonstrate the effectiveness of the proposed approach, and our method significantly reduces annotation effort while maintaining competitive recognition performance.
KeywordText recognition Reinforcement learning Training Feature extraction Annotations Probability distribution Predictive models Semi-supervised scene text recognition embedding reinforcement learning
DOI10.1109/TIP.2021.3051485
Indexed BySCI
Language英语
Funding ProjectResearch and Development Projects in the Key Areas of Guangdong Province[2019B010153001] ; National Natural Science Foundation of China[61772527] ; National Natural Science Foundation of China[61806200] ; National Natural Science Foundation of China[62006230] ; National Natural Science Foundation of China[61876086]
Funding OrganizationResearch and Development Projects in the Key Areas of Guangdong Province ; National Natural Science Foundation of China
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000621399700002
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/43345
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorChen, Yingying
Affiliation1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Gao, Yunze,Chen, Yingying,Wang, Jinqiao,et al. Semi-Supervised Scene Text Recognition[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2021,30:3005-3016.
APA Gao, Yunze,Chen, Yingying,Wang, Jinqiao,&Lu, Hanqing.(2021).Semi-Supervised Scene Text Recognition.IEEE TRANSACTIONS ON IMAGE PROCESSING,30,3005-3016.
MLA Gao, Yunze,et al."Semi-Supervised Scene Text Recognition".IEEE TRANSACTIONS ON IMAGE PROCESSING 30(2021):3005-3016.
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