vmaitComputational Mathematics and Information TechnologiesComputational Mathematics and Information Technologies2587-8999Донской государственный технический университет10.23947/2587-8999-2025-9-1-52-60vmait-185Research ArticleИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИINFORMATION TECHNOLOGIESАвтоматическое распознавание значений глубины на лоцманских картах с использованием методов глубокого обученияAutomatic Depth Value Recognition on Pilot Charts Using Deep Learning Methodshttps://orcid.org/0000-0002-3194-6144РахимбаеваЕ. О.RakhimbaevaE. O.

Елена Олеговна Рахимбаева, аспирант, ассистент кафедры программного обеспечения вычислительной тех- ники и автоматизированных систем 

344003, г. Ростов-на-Дону, пл. Гагарина, 1

Elena O. Rakhimbaeva, Postgraduate student, Assistant lecturer of the Department of “Computer Engineering and Automated Systems Software”

1, Gagarin Sq., Rostov-on-Don, 344003

lena_rahimbaeva@mail.ruhttps://orcid.org/0009-0009-7765-5033АлышовТ. А.AlyshovT. A.

Таджаддин Аледдин оглы Алышов, магистрант кафедры математики и информатики 

344003, г. Ростов-на-Дону, пл. Гагарина, 1

Tadjaddin A. Alyshov, Master’s Degree student of the Department of “Mathematics and Computer Science”

1, Gagarin Sq., Rostov-on-Don, 344003

taci2002@mail.ruhttps://orcid.org/0000-0002-2639-7451БеловаЮ. B.BelovaYu. V.

Юлия Валериевна Белова, кандидат физико-математических наук, доцент кафедры математики и информатики 

344003, г. Ростов-на-Дону, пл. Гагарина, 1

Yulia V. Belova, Candidate of Physical and Mathematical Sciences, Associate Professor of the Department of “Mathematics and Computer Science”

1, Gagarin Sq., Rostov-on-Don, 344003

 

yvbelova@yandex.ruДонской государственный технический университетРоссияDon State Technical UniversityRussian Federation202505042025915260Copyright © Рахимбаева Е.О., Алышов Т.А., Белова Ю.B., 20252025Рахимбаева Е.О., Алышов Т.А., Белова Ю.B.Rakhimbaeva E.O., Alyshov T.A., Belova Y.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.cmit-journal.ru/jour/article/view/185Введение

Введение. Рассматривается проблема автоматического распознавания текста на изображениях, в частности задача извлечения информации о глубинах с лоцманских карт. Актуальность данной задачи обусловлена необходимостью автоматизации обработки больших объемов картографических данных для построения карты глубин, пригодной для математического моделирования гидродинамических и гидробиологических процессов. Целью работы является разработка программного средства (ПС) LocMap, предназначенного для автоматического обнаружения и распознавания значений глубин, представленных в виде чисел на изображениях лоцманских карт. Материалы и методы. В работе использованы методы глубокого обучения, а именно сверточные нейронные сети ResNet для извлечения признаков, алгоритм дифференцируемой бинаризации DB для обнаружения текста и архитектура Scene Text Recognition with a Single Visual Model (SVTR) для распознавания текста.

Результаты исследования

Результаты исследования. Разработанное ПС позволяет загружать изображения лоцманских карт, выполнять предобработку, обнаруживать и распознавать значения глубин, выделять их на изображении и сохранять результаты в текстовый файл. Результаты тестирования показали, что разработанная система обеспечивает высокую точность распознавания значений глубин на лоцманских картах.

Обсуждение и заключение

Обсуждение и заключение. Полученные результаты демонстрируют практическую значимость разработанного решения для автоматизации обработки лоцманских карт.

information from pilot charts. The relevance of this task is driven by the need to automate the processing of large volumes of cartographic data to create depth maps suitable for mathematical modelling of hydrodynamic and hydrobiological processes. The objective of this work is to develop the software tool LocMap, designed for the automatic detection and 52     recognition of depth values represented as numbers on pilot chart images.

Materials and Methods

Materials and Methods. The study employs deep learning methods, including convolutional neural networks (ResNet) for feature extraction, the Differentiable Binarization (DB) algorithm for text detection, and the Scene Text Recognition with a Single Visual Model (SVTR) architecture for text recognition.

Results

Results. The developed software allows users to upload pilot chart images, perform preprocessing, detect and recognize depth values, highlight them in the image, and save the results in a text file. Testing results demonstrated that the system ensures high accuracy in recognizing depth values on pilot charts.

Discussion and Conclusion. The obtained results highlight the practical significance of the developed solution for automating the processing of pilot charts.

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The authors declare that there are no conflicts of interest present.