CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习
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
Conference NameInternational Conference on Document Analysis and Recognition
Conference Date2017.11.9-15
Conference PlaceKyoto, Japan
AbstractIn 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.
Other Abstract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。
KeywordMulti-task Learning Sepmdlstm Script Identification Language Identification Handwritten Text Recognition
Document Type会议论文
Recommended Citation
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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