This article describes the recognition of bank card information. Recognizing an object with a camera is one of the most important tasks at the moment. Recognizing credit card data at the same time is a rather complex algorithmic task, but at the moment the implementation of this task is very relevant and in-demand due to the increase in the number of payment transactions via mobile devices. The implementation of this task can save a person from having to enter most of the data when making online payments. The fundamental difficulties of this problem are discussed and methods for solving it are proposed. The problem under consideration is solved for the case of application on mobile devices, which imposes strict requirements for computational complexity. The article presents the results of a formal analysis of the performance and accuracy of the proposed algorithm. The error spectrum of the recognition system as a whole shows that the proposed algorithm solves the problem with the required accuracy. The main question that was investigated at this work: is it possible to use the Tesseract OCR library for text recognition from video images, for example, timecode? That is, digital time data embedded in the footage images. This is important for the automation of individual procedures for video technical expert studies. Object recognition by the camera is one of the most important tasks at the moment. The fundamental difficulties of this problem are discussed and methods for its solution are proposed. The article presents the results of a formal analysis of the performance and accuracy of the proposed algorithm. The spectrum of errors of the recognition system as a whole shows that the proposed algorithm solves the problem with the required accuracy.

Speech recognition has various applications, including human-machine interaction, sorting phone calls by gender classification, categorizing videos with tags, and so on. Currently, machine learning is a popular field that is widely used in various fields and applications, taking advantage of the latest developments in digital technologies and the advantages of data storage capabilities from electronic media. In this article, we will focus on voice gender recognition for a class of text-dependent systems using the Dynamic time distortion (DTW) algorithm and for a class of text-independent systems, the Gaussian mixture model. With this method, it is possible to distinguish a person's voice with the highest accuracy, since the components of Gaussian mixtures can simulate the personality of the voice. The article presents the results of testing the algorithm, and concludes that the Gaussian mixture model is applicable to solving the problem of identifying a person by voice.

Speech recognition has various applications, including human-machine interaction, sorting phone calls by gender classification, categorizing videos with tags, and so on. Currently, machine learning is a popular field that is widely used in various fields and applications, taking advantage of the latest developments in digital technologies and the advantages of data storage capabilities from electronic media. In this article, we will focus on voice gender recognition for a class of text-dependent systems using the Dynamic time distortion (DTW) algorithm and for a class of text-independent systems, the Gaussian mixture model. With this method, it is possible to distinguish a person's voice with the highest accuracy, since the components of Gaussian mixtures can simulate the personality of the voice. The article presents the results of testing the algorithm, and concludes that the Gaussian mixture model is applicable to solving the problem of identifying a person by voice.