User interface modeling for convolutional neural network for complex character recognition

IF 0.4 Q4 MATHEMATICS, APPLIED Journal of Applied Mathematics & Informatics Pub Date : 2023-06-16 DOI:10.37791/2687-0649-2023-18-3-105-114
A. E. Trubin, Filipp A. Mastyaev, A. V. Batishchev, Aleksey I. Zaytsev, S. A. Aleksakhina
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Abstract

In this article, we design a user interface for a prototype desktop application using the capabilities of the author’s neural network for recognizing texts in Japanese written by one of the two Japanese alphabets – katakana or hiragana. During the design, the UML notation, a Use-Case Diagram, was used to build scenarios for using the program, and the BPMN notation was used to describe a program’s main algorithm. In the beginning of this article short versions of previous two articles were also given – the basics of proposed method for preprocessing of machine learning data and the main parameters of the proposed convolutional neural network model including its efficiency against reference model EfficientNetB0. In the work, the principles and the tool base for designing the interface of the software solution were defined, the scenarios for using the program, the algorithms of the program were designed, a prototype of the user interface was created.
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复杂字符识别的卷积神经网络用户界面建模
在本文中,我们为原型桌面应用程序设计了一个用户界面,使用作者的神经网络的功能来识别由两种日语字母(片假名或平假名)之一编写的日语文本。在设计过程中,使用UML符号(用例图)来构建使用程序的场景,使用BPMN符号来描述程序的主要算法。在本文的开头,还给出了前两篇文章的简短版本-所提出的机器学习数据预处理方法的基础知识和所提出的卷积神经网络模型的主要参数,包括其对参考模型EfficientNetB0的效率。在工作中,定义了软件解决方案界面设计的原则和工具库,设计了程序的使用场景和算法,创建了用户界面原型。
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