Development of the Neural Network Based Recognition Methods at V.L. Arlazarov’s Scientific School

Abstract—This work is devoted to methods for improving the quality of artificial neural networks, developed by a group of scientists led by V.L. Arlazarov. It describes various approaches both to training the networks themselves, from applying regularization to using new types of layers, and to preparing training data. This paper presents an effective method of data augmentation, as well as a neural network method for generating text data for training, which allows taking into account the properties of the language model in text recognition tasks. The applicability of metric networks to solving the classification problem and methods for generating and balancing training data for training such networks by creating Siamese and triplet schemes using contrastive loss and triplet loss functions are described. A separate section is devoted to research on the use of the fast Hough transform in neural networks as one of the methods for extracting nontrivial features. The paper also describes studies of a block convolutional layer, a method for constructing estimates of the presence of cuts between characters in the text segmentation problem, and a method for increasing the generalization ability of networks using orthogonalization.