Knowledge Commons of Institute of Automation,CAS
Cross-lingual text image recognition via multi-task sequence to sequence learning | |
Chen, Zhuo1,2; Yin, Fei1,2; Zhang, Xu-Yao1,2; Yang, Qing1,2; Liu, Cheng-Lin1,2,3 | |
2021-05 | |
会议名称 | 2020 25th International Conference on Pattern Recognition (ICPR) |
会议日期 | 2021-1-10 |
会议地点 | Milan, Italy |
摘要 | This paper considers recognizing texts shown in a source language and translating into a target language, without generating the intermediate source language text image recognition results. We call this problem Cross-Lingual Text Image Recognition (CLTIR). To solve this problem, we propose a multi-task system containing a main task of CLTIR and an auxiliary task of Mono-Lingual Text Image Recognition (MLTIR) simultaneously. Two different sequence to sequence learning methods, a convolution based attention model and a Bidirectional Long Short-Term Memory (BLSTM) model with Connectionist Temporal Classification (CTC), are adopted for these tasks respectively. We evaluate the system on a newly collected Chinese-English bilingual movie subtitle image dataset. Experimental results demonstrate the multi-task learning framework performs superiorly in both languages. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 文字识别与文档分析 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/45032 |
专题 | 多模态人工智能系统全国重点实验室_模式分析与学习 |
通讯作者 | Chen, Zhuo |
作者单位 | 1.National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.CAS Center for Excellence in Brain Science and Intelligence Technology |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Chen, Zhuo,Yin, Fei,Zhang, Xu-Yao,et al. Cross-lingual text image recognition via multi-task sequence to sequence learning[C],2021. |
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09412281.pdf(2273KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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