Simultaneous Script Identification and Handwriting Recognition via Multi-Task Learning of Recurrent Neural Networks

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	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
会议日期	2017.11.9-15
会议地点	Kyoto, Japan
关键词	Multi-task Learning Sepmdlstm Script Identification Language Identification Handwritten Text Recognition
英文摘要	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.
语种	英语
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/20012]
专题	自动化研究所_模式识别国家重点实验室_模式分析与学习团队
作者单位	1.中国科学院自动化研究所 2.中国科学院大学
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]. 见:. Kyoto, Japan. 2017.11.9-15.