CASIA OpenIR  > 多模态人工智能系统全国重点实验室
Cross-Lingual Text Image Recognition via Multi-Hierarchy Cross-Modal Mimic
Chen, Zhuo1,2; Yin, Fei1,2; Yang, Qing1,2; Liu, Cheng-Lin1,2
Source PublicationIEEE TRANSACTIONS ON MULTIMEDIA
ISSN1520-9210
2023
Volume25Pages:4830-4841
Corresponding AuthorLiu, Cheng-Lin(liucl@nlpr.ia.ac.cn)
AbstractOptical character recognition and machine translation are usually studied and applied separately. In this paper, we consider a new problem named cross-lingual text image recognition (CLTIR) that integrates these two tasks together. The core of this problem is to recognize source language texts shown in images and transcribe them to the target language in an end-to-end manner. Traditional cascaded systems perform text image recognition and text translation sequentially. This can lead to error accumulation and parameter redundancy problems. To overcome these problems, we propose a multihierarchy cross-modal mimic (MHCMM) framework for end-to-end CLTIR, which can be trained with a massive bilingual text corpus and a small number of bilingual annotated text images. In this framework, a plug-in machine translation model is used as a teacher to guide the CLTIR model for learning representations compatible with image and text modes. Via adversarial learning and attention mechanisms, the proposed mimic method can integrate both global and local information in the semantic space. Experiments on a newly collected dataset demonstrate the superiority of the proposed framework. Our method outperforms other pipelines while containing fewer parameters. Additionally, the MHCMM framework can utilize a large-scale bilingual corpus to further improve the performance efficiently. The visualization of attention scores indicates that the proposed model can read text images in a fashion similar to the machine translation model reading text tokens.
KeywordCross-lingual text image recognition cross-modal mimic multihierarchy mimic
DOI10.1109/TMM.2022.3183386
WOS KeywordSCENE TEXT
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program[2020AAA0108003] ; National Natural Science Foundation of China[61733007] ; National Natural Science Foundation of China[61721004]
Funding OrganizationNational Key Research and Development Program ; National Natural Science Foundation of China
WOS Research AreaComputer Science ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS IDWOS:001097340300016
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/55205
Collection多模态人工智能系统全国重点实验室
Corresponding AuthorLiu, Cheng-Lin
Affiliation1.Chinese Acad Sci, Natl Lab Pattern Recognit NLPR, 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
Chen, Zhuo,Yin, Fei,Yang, Qing,et al. Cross-Lingual Text Image Recognition via Multi-Hierarchy Cross-Modal Mimic[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2023,25:4830-4841.
APA Chen, Zhuo,Yin, Fei,Yang, Qing,&Liu, Cheng-Lin.(2023).Cross-Lingual Text Image Recognition via Multi-Hierarchy Cross-Modal Mimic.IEEE TRANSACTIONS ON MULTIMEDIA,25,4830-4841.
MLA Chen, Zhuo,et al."Cross-Lingual Text Image Recognition via Multi-Hierarchy Cross-Modal Mimic".IEEE TRANSACTIONS ON MULTIMEDIA 25(2023):4830-4841.
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