A Method to Identify the Cause of Misrecognition for Offline Handwritten Japanese Character Recognition using Deep Learning
A Method to Identify the Cause of Misrecognition for Offline Handwritten Japanese Character Recognition using Deep Learning
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DOI：
10.5220/0008949004460452
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
K. Gyohten;H. Ohki;Toshiya Takami
中科院分区：
文献类型：
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作者：
K. Gyohten;H. Ohki;Toshiya Takami
在这项研究中，我们提出了一种方法来确定脱机手写字符识别中使用卷积神经网络（CNN）的误识别的原因。在我们的方法中，CNN不仅学习通过应用图像处理方法增强的字符图像，还学习从具有笔画结构的字符模型生成的字符图像。利用这些字符模型，该方法可以生成缺少一个笔画的字符图像。通过学习缺少笔画的增强字符图像，CNN可以识别待识别字符中每个笔画的存在。随后，通过向最终层添加密集层并学习字符图像，获得用于离线手写字符识别的CNN成为可能。所获得的CNN具有可以表示笔画的存在的节点，并且可以识别哪些笔画是错误识别的原因。针对440种日文字符的字符识别实验证实了该方法的有效性。
: In this research, we propose a method to identify the cause of misrecognition in offline handwritten character recognition using a convolutional neural network (CNN). In our method, the CNN learns not only character images augmented by applying an image processing method, but also those generated from character models with stroke structures. Using these character models, the proposed method can generate character images which lack one stroke. By learning the augmented character images lacking a stroke, the CNN can identify the presence of each stroke in the characters to be recognized. Subsequently, by adding dense layers to the final layer and learning the character images, obtaining the CNN for the offline handwritten character recognition becomes possible. The obtained CNN has nodes that can represent the presence of the strokes and can identify which strokes are the cause of misrecognition. The effectiveness of the proposed method is confirmed from character recognition experiments targeting 440 types of Japanese characters.