中国科学院自动化研究所机构知识库

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.

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。
首先，我们使用卷积神经网络与可分离的MDLSTM对输入的文本行图像进行编码。然后提取出的特征分别输入到语种分类模块与多语种文本识别模块中。
网络的全部参数都是通过多任务学习进行端到端的更新，其中语种分类模块与文本识别模块的损失函数分别为负似然对数（NLL）与连接时序分类（CTC）。
在IAM（英语）、Rimes（法语）和IFN/ENIT（阿拉伯语）三个数据集进行实验，我们的网络的语种分类正确率为99.92%，字符集识别正确率分别为88.85，91.71，85.06。