?
РАСПОЗНАВАНИЕ РУКОПИСНЫХ ГРАФИЧЕСКИХ ЭЛЕМЕНТОВ И МУЛЬТИЯЗЫЧНОГО ТЕКСТА: ОБЗОР СОВРЕМЕННЫХ ПОДХОДОВ, БАЗОВЫЙ МЕТОД И ПЕРСПЕКТИВЫ РАЗВИТИЯ
С. 659–669.
This article presents an overview of modern methods for solving the problem of recognizing handwritten content, including multilingual text (Russian and English) and graphical elements such as block diagrams consisting of rectangles and arrows, with a focus on the recognition of whiteboard images. For multilingual handwritten text, architectures capable of processing multiple languages are considered, and for block diagrams - methods for recognizing shapes and structure. In addition, the paper proposes a baseline model in which EasyOCR and TrOCR are used for text recognition, and OpenCV-based algorithms that extract shape contours are used for analyzing graphical elements. The purpose of this model is to assess the complexity of solving the given problem using publicly available tools with minimal preparation. The baseline model is focused on the specific task of recognizing content on a whiteboard, and the results are generated in XML format for further processing (e.g., visualization). The system was tested on a specially collected dataset of whiteboard images. The article also discusses the main challenges of the recognition task and outlines promising directions for improving the accuracy and quality of the solution.
In book
Йошкар-Ола: Поволжский государственный технологический университет, 2025.
Aleksei Samarin, Nazarenko A., Nazarenko D. et al., Machine Learning and Knowledge Extraction 2026 Vol. 8 No. 10 Article 330
Automated microscopic image analysis is essential for quantitative microbiology, yet manual acquisition and generic recognition models struggle with morphological variability and scene artifacts. We address both ends of this pipeline. First, we present a low-cost, open-source acquisition system, based on an Arduino UNO R3 microcontroller and NEMA 17 stepper motors, that automates focusing, magnification, and ...
Added: October 14, 2026
Aleksei Samarin, Aleksei Toropov, Alexander Savelev et al., Pattern Recognition and Image Analysis 2024 Vol. 34 No. 4 P. 1044–1052
This paper presents a study aimed at improving the detection quality of small-sized microorganisms under challenging microscopic conditions through the application of a lightweight combined image preprocessing model. We focused on the task of detecting diplococci in images obtained through dynamic sample microscopy. The proposed approach employs predefined filters for image preprocessing, combined with the ...
Added: September 21, 2026
Aleksei Samarin, Aleksei Toropov, Alexander Savelev et al., , in: Pattern Recognition. ICPR 2024 International Workshops and Challenges. Kolkata, India, December 1, 2024, Proceedings, Part III. LNCS, volume 15616.: Cham: Springer, 2025. P. 308–320.
This research explores an innovative approach to enhancing the accuracy of detecting small microorganisms in complex microscopic environments. Our study introduces a streamlined, hybrid image pre-processing model specifically designed to address the challenges of identifying diplococci in live microscopy of dynamic samples. By integrating pre-defined filtering techniques with predictive adjustments for optimal applicability, our method ...
Added: September 21, 2026
Lebedev O. B., Черкасов Р. И., Известия ЮФУ. Технические науки 2025 № 5(247) С. 254–276
This paper considers the application of artificial intelligence technologies, in particular computer vision, in visual information processing systems. A comprehensive analysis of neural network approaches to solving computer vision problems is carried out, including systematization of key types of problems: image classification, object detection and semantic segmentation. The architectural principles of convolutional neural networks are ...
Added: September 10, 2026
Vasilev A., Kapitanov A., Roman Solovyev et al., PeerJ Computer Science 2026 Vol. 12 Article 3724
This article introduces MinMAE, a novel activation calibration method for Post-Training Quantization (PTQ) that significantly reduces accuracy loss in Convolutional Neural Networks (CNN). Motivated by the need for high-fidelity quantization without costly retraining, MinMAE directly minimizes the Mean Absolute Error (MAE) between original and dequantized activations, making it robust to outliers that degrade standard methods. ...
Added: May 3, 2026
Karpukhin I., Shipilov F., Savchenko A., Neurocomputing 2026 Vol. 672 Article 132771
Forecasting multiple future events within a given time horizon is essential for applications in finance, retail,
social networks, and healthcare. This problem is typically addressed using Marked Temporal Point Processes
(MTPP), which provide a principled framework for modeling both event timing and event labels. While most
existing research focuses on predicting only the next event, forecasting distant future ...
Added: February 25, 2026
Moshkin A., Лапутин Ф. А., Сидоров И. В., DIGITAL DIAGNOSTICS 2024 Т. 5 № S1 С. 40–42
BACKGROUND: Ovarian reserve reflects a woman's ability to successfully realize reproductive function. The assessment of ovarian reserve is an urgent task for clinical practice [1] and is important in scientific research. The use of computerized diagnostic image processing methods can accelerate and facilitate the performance of routine tasks in clinical practice. Their use in retrospective ...
Added: February 21, 2026
Zemlyakov M., В кн.: Источниковедение в современной медиевистике. Материалы третьей всероссийской научной конференции. Москва, 17-18 июня 2025 г.: М.: Аквилон, 2025. С. 82–86.
В публикации представлен первый на русском языке обзор общедоступных палеографических тренажёров в сети Интернет, созданных учёными и программистами из Австралии, Франции, Великобритании, США, Швейцарии за последние 30 лет. Также особое внимание уделено проблеме распознавания рукописных текстов Средневековья на базе технологии HTR и искусственного интеллекта в России и за рубежом ...
Added: June 26, 2025
, in: Lecture Notes in Electrical EngineeringVol. 489: Applied Physics, System Science and Computers II.: Springer, 2019. P. 87–100.
To give shift in safety protocols, we have employed advanced deep learning algorithms and frameworks to construct an innovative AI model. The designed model detects the usage of personal protective equipment (PPE) by workers in high-risk industries such as construction and manufacturing. We have used Google’s TensorFlow object detection API to modify and train a model for ...
Added: December 30, 2024
Kseniia Prokudina, Mikhail Skriplyonok, Alexander Vostrikov, , in: 2024 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), 20-24 May 2024.: IEEE, 2024. P. 865–869.
Added: November 26, 2024
A. V. Savchenko, L. V. Savchenko, Information Sciences 2023 Vol. 648 Article 119540
This article introduces the novel technique to reduce the computation time for classifying a sequence of observations (frames), such as a video stream, where each observation is described by high-dimensional embeddings extracted by a deep neural network. By using the methodology of granular computing, an observed sequence is represented at various scales using different frame ...
Added: August 27, 2023
Saleh H., Saleh S., Nathan Teyou Toure et al., , in: 2021 IEEE Concurrent Processes Architectures and Embedded Systems Virtual Conference (COPA).: IEEE, 2022. Ch. 7 P. 1–6.
The annual number of road deaths is still increasing, especially in less developed and developing countries. Road accidents are the 5th cause of death and the leading reason for death among young people between 5 and 29 years of age in 2030. In this study, a robust solution is implemented by integrating object recognition with ...
Added: October 31, 2022
Belykh M. Vladimirovna, Belov A. Vladimirovich, , in: 2022 International Conference on Interdisciplinary Research in Technology and Management, IRTM 2022 - Proceedings.: IEEE, 2022. P. 1–4.
Added: July 15, 2022
Savchenko A., Belova N. S., Expert Systems with Applications 2022 Vol. 207 Article 117885
In this paper, the computational complexity of the probabilistic neural network for the classification of high-dimensional data is improved. At first, the class probability densities are estimated by using only a few principal components of an observed point. The Gaussian–Parzen kernel is replaced by the orthogonal series estimates of class-conditional densities for each principal component using the Fourier series to speed ...
Added: June 29, 2022
Savchenko A., Demochkin K., Grechikhin I., Pattern Recognition 2022 Vol. 121 Article 108248
In this paper, a user modeling task is examined by processing mobile device gallery of photos and videos. We propose a novel engine for preferences prediction based on scene recognition, object detection and facial analysis. At first, all faces in a gallery are clustered, and all private photos and videos with faces from large clusters ...
Added: August 19, 2021
Solovyev R. A., Telpukhov D. V., Romanova I. I. et al., , in: Proceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021.: IEEE, 2021. P. 2029–2034.
The paper proposes methodology for transferring architecture of modern neural network CenterNet to FPGA. CenterNet is a OneStage object detector that is used to detect and locate objects in images. Although this neural network has simple decoder, it shows good performance in terms of accuracy. Very high operation speed of the neural network hardware is ...
Added: August 8, 2021
Belov A. V., Belykh M., Tofayli S., , in: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTERDISCIPLINARY RESEARCH IN TECHNOLOGY AND MANAGEMENT (IRTM, 2021).: CRC Press, 2021. Ch. 32 P. 199–203.
The article deals with the problem of collecting information about railway traffic lights located on the territory of the Russian Federation. To solve this problem, a trained neural network is used that detects railway traffic lights in high-definition images. These images were taken by a camera installed on a train carriage, continuously photographing the area ...
Added: July 15, 2021
Rzaev E., Khanaev A., Amerikanov A., , in: 2021 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM).: IEEE, 2021. P. 719–723.
Added: July 4, 2021
Savchenko A., Information Sciences 2021 Vol. 560 P. 370–385
A novel image recognition algorithm based on sequential three-way decisions is introduced to speed up the inference in a convolutional neural network. In contrast to the majority of existing studies, our approach does not require a special procedure to train a neural network, and thus it can be used with arbitrary architectures including pre-trained convolutional ...
Added: February 25, 2021