title: ÐÐµÑÐ¾Ð´Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð² Ð·Ð°Ð´Ð°ÑÐµ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÑÐºÐ¾Ð¿Ð¸ÑÐ½Ð¾Ð³Ð¾ ÑÐµÐºÑÑÐ°
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Machine learning in the handwritten text recognition problem
Creators: name: Ð§ÐµÑÐ½Ð¸ÐºÐ¾Ð², Ð¡ÑÐµÐ¿Ð°Ð½
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ ÑÑÐºÐ¾Ð¿Ð¸ÑÐ½Ð¾Ð³Ð¾ ÑÐµÐºÑÑÐ°
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
handwritten text recognition
computer vision
neural networks
machine learning

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description: Today, in many subject areas, there is a significant amount of handwritten documents and the need to systematize the information contained in it, presenting it in machine-readable form. The complexity of this operation lies in the fact that, unlike printed ones, handwritten fonts exhibit great variability both over time and within one period. Currently existing solutions have an accuracy that is unacceptable in practice, so the problem is relevant at the moment. In this paper, we study the problem of recognizing handwritten Cyrillic text. Previously proposed relevant solutions to this problem are considered, the general structure of the recognition algorithm, methods for solving subtasks and software implementation of individual modules are proposed. The developed text recognition method is based on identifying individual words in the text and recognizing them using a neural network. To train the network, a method is proposed for generating a synthetic sample of arbitrary size, which ensures variability in handwriting and the ability to take into account blots in the text. To generate a synthetic sample, an auxiliary word recognition model is used and a character sample validation method is applied. As part of the work, the problems of image preprocessing, highlighting word boundaries on it, and directly training the model for text recognition are solved. The problem of constructing the life cycle of two neural networks is solved: the main and auxiliary ones.