Study on Recognition Mechanism of Transformation Character Pattern by Neural Network

人是善于识别文字、声音和图像模式的人。然而，即使引入最新的图像处理技术，模式识别也是困难的。基于神经网络的模式识别研究正是在这样的背景下积极开展的。本研究是对汉字体在三维空间运动时所见字形识别机制的总结。如果移动了角色的视角，角色将接受几何转换。如果方面接近角色平面的中心，则角色阵列接收透视。目前，还没有提出用神经网络来识别这种变换特征。将复制学习方法引入神经网络，提出了一种能够识别变换字符的识别机制。本研究的摘要如下。(1)通过实验，阐明了输入模式分辨率与方位运动中字符识别率之间的关系。本研究起到了阐明体运动与文字识别互补的作用。(2)本研究提出一个复制学习模型，仅需学习标准字，即可将标准字从重叠字中辨识出来。本研究计划未来发展为污垢文字、图像、文字之重迭文字之分离与辨识。

The person is the good at recognition of the character, the sound, and the image pattern. However, even if the latest image processing technology is introduced, the pattern recognition is difficult. The study on the pattern recognition by the neural network is actively done from such a background. This study is a summary of the recognition mechanism of the character pattern seen when the aspect in the space three dimensions moves. If the aspect by which the character is seen is moved, the character receives geometrical conversion. If the aspect approaches the center of the character plane, the character pattern receives the perspective. At present, the neural network by Which such a transformation character can be recognized is not proposed. This study introduces the copy learning method into the neural network, and proposes the recognition mechanism which can recognize the transformation character. The summary of this study is as follows.(1) The relation between the input pattern resolution and the character recognition rate in the aspect movement was clarified through the experiment. This study plays elucidation of complementary of the aspect movement and the character recognition the role.(2) This study proposes a copy-learning model which can recognize standard characters from superimposed characters by only learning the standard characters. This study is scheduled development into the separation and the recognition of the superimposed character of the dirt character, the picture, and the character for the future.