Building an Optimal Convolutional Neural Network Model for Solving Complex Character Recognition Problems

Abstract

The purpose of the study is aimed at developing a lighter architecture of a convolutional neural network model that will cope with the narrowly focused task of recognizing complex characters better than large-scale and well-known ones. As the source data, the characters of the Japanese language are used, consisting of two syllabic alphabets: hiragana and katakana, which are the most complex, since their writing style is characterized by a large number of features and similarity of characters, which greatly complicates the task of their classification and recognition. The author's model of a convolutional neural network is designed in the article, consisting of four convolutional layers, three layers of subdiscretization and three layers of exclusion. The developed model was compared with one of the most popular models of the EfficientNetBO neural network from the point of view of their architecture and the results of work on the same data. To implement its own convolutional neural network model, the classic Keras + Tensorflow bundle was used, since these libraries provide the most convenient tools for working in the field of machine learning. The result of the conducted research is the developed technology of fast and accurate recognition of complex symbols based on a convolutional neural network, which can become the basis of a software product in the field of computer vision.