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Handwriting prediction based character recognition using recurrent neural network

机译:基于递归神经网络的基于手写预测的字符识别

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Humans are said to unintentionally trace handwriting sequences in their brains based on handwriting experiences when recognizing written text. In this paper, we propose a model for predicting handwriting sequence for written text recognition based on handwriting experiences. The model is first trained using image sequences acquired while writing text. The image features of sequences are self-organized from the images using Self-Organizing Map. The feature sequences are used to train a neuro-dynamics learning model. For recognition, the text image is input into the model for predicting the handwriting sequence and recognition of the text. We conducted two experiments using ten Japanese characters. The results of the experiments show the effectivity of the model.
机译:据说人类在识别书面文本时会根据笔迹经验无意中追踪大脑中的笔迹序列。在本文中,我们提出了一种基于笔迹经验的笔迹序列预测模型,以用于手写文字识别。首先使用在编写文本时获取的图像序列来训练模型。序列的图像特征是使用“自组织映射”根据图像进行自组织的。特征序列用于训练神经动力学学习模型。为了识别,将文本图像输入到模型中以预测手写顺序和文本识别。我们使用十个日语字符进行了两个实验。实验结果表明了该模型的有效性。

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