Knowledge Commons of Institute of Automation,CAS
Simultaneous Script Identification and Handwriting Recognition via Multi-Task Learning of Recurrent Neural Networks | |
Chen, Zhuo1,2; Wu, Yichao1,2; Yin, Fei1; Liu, Chenglin1,2 | |
2017 | |
会议名称 | International Conference on Document Analysis and Recognition |
会议日期 | 2017.11.9-15 |
会议地点 | Kyoto, Japan |
摘要 | In this paper, we propose a method for simultaneous script identification and handwritten text line recognition in multi-task learning framework. Firstly, we use Separable Multi-Dimensional Long Short-Term Memory (SepMDLSTM) to encode the input text line images based on convolutional feature extraction. Then, the extracted features are fed into two classification modules for script identification and multi-script text recognition, respectively. All the network parameters are trained end-to-end by multi-task learning where the script identification task and the text recognition task are aimed to minimize the Negative Log Likelihood (NLL) loss and Connectionist Temporal Classification (CTC) loss, respectively. We evaluated the performance of the proposed method on handwritten text line datasets of three languages, namely, IAM (English), Rimes (French) and IFN/ENIT (Arabic). Experimental results demonstrate the multitask learning framework performs superiorly for both script identification and text recognition. Particularly, the accuracy of script identification is higher than 99.9% and the character error rate (CER) of text recognition is even lower than that of some single-script text recognition systems. |
其他摘要 | 该工作提出了一种在多任务学习框架(multi-task)中同时进行语种分类与文本识别的方法。 |
关键词 | Multi-task Learning Sepmdlstm Script Identification Language Identification Handwritten Text Recognition |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/20012 |
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 |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Chen, Zhuo,Wu, Yichao,Yin, Fei,et al. Simultaneous Script Identification and Handwriting Recognition via Multi-Task Learning of Recurrent Neural Networks[C],2017. |
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ICDAR2017-Simultaneo(467KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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