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DOI: 10.14489/vkit.2025.02.pp.046-054
Яценко Д. В., Граецкая О. В., Смирнов К. В.
НАБОР ДАННЫХ РУКОПИСНЫХ СИМВОЛОВ РУССКОГО ЯЗЫКА
(c. 46-54)
Аннотация. Наличие наборов данных – важный фактор для развития машинного обучения. Сегодня опубликовано не так много наборов данных, содержащих национальные рукописные символы. В статье представлен набор данных, описывающий рукописные символы русского языка. Датасет содержит образцы написания символов 12 писателей, прописные и строчные символы, а также цифры – итого 76 классов символов. Дополнительно набор данных включает некоторые образцы написания целых слов. Данные о символах представлены в виде координат траекторий движения пера писателя в процессе начертания символа. Описана методология сбора и обработки данных. Кроме того, точность распознавания базовой моделью LeNet-5 представленного набора данных сравнивается с близким по количеству классов набором данных EMNIST. Для представленного датасета качество немного хуже, чем для EMNIST, однако датасет при этом обладает важными свойствами: данные заданы не только матричным видом, но и траекторией движения. Это позволило учитывать особенности движения пера, что может быть использовано не только в целях классификации символов, но и для распознавания персональных особенностей почерка, переноса стиля письма, графологического анализа.
Ключевые слова: набор данных; нейронная сеть; рукописные символы; траектория написания.
Iatsenko D. V., Graeckaja O. V., Smirnov K. V.
A DATA SET OF HANDWRITTEN CHARACTERS OF THE RUSSIAN LANGUAGE
(pp. 46-54)
Abstract. The availability of open data sets is an important factor for the development of machine learning. There are not many data sets that contain national handwritten characters published today. The paper provides an overview of the published data sets and their comparative analysis. As can be seen from the review, there are very few Russian-speaking data sets in the presented ones, and most of them are a set of images of handwritten or printed characters. The article presents a data set collected by the authors describing handwritten symbols of the Russian language. This data set contains character writing samples from 12 writers and contains uppercase and lowercase characters, as well as numbers – a total of 76 character classes. The data set additionally includes some spelling patterns of whole words. The information about the symbols are presented in the form of coordinates of the trajectories of the writer's pen during the process of drawing the symbol. There is also a program code similar to the EMNIST data set that allows you to convert character trajectories into images. There is also a program code that allows you to convert character trajectories into images, similar to the EMNIST data set. The paper describes the methodology of data collection and processing. In addition, the recognition accuracy of the basic LeNet-5 model of the presented data set is compared with the EMNIST data set, which is similar in number of classes. For the presented data set, the quality is shown to be slightly worse than for EMNIST. However, the presented data set, at the same time, has important properties – since the data is set not only by the image matrix form, but also by the trajectory of movement – this allows you to take into account the peculiarities of pen movement, which can be used not only for the purpose of classifying characters, but also for the purpose of recognizing personal features of handwriting, transferring writing style, graphological analysis.
Keywords: Data set; Neural network; Handwritten characters; Trajectory of the writer's pen.
Д. В. Яценко, О. В. Граецкая (Южный Федеральный Университет, Ростов-на-Дону, Россия) E-mail:
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К. В. Смирнов (Университет Иннополис, Иннополис, Россия)
D. V. Iatsenko, O. V. Graeckaja (South Federal University, Rostov-on-Don, Russia) E-mail:
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K. V. Smirnov (Innopolis University, Innopolis, Russia)
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1. Botta M., Giordana A., Saitta L. (1993). Learning fuzzy concept definitions. Proceedings of the 2nd IEEE International Conference on Fuzzy Systems, 1, 18 – 22. Piscataway: IEEE.
2. Frey P. W., Slate D. J. (2004). Letter recognition using Holland-style adaptive classifiers. Machine Learning, 6, 161 – 182.
3. Peltonen J., Klami A., Kaski S. (2004). Improved learning of riemannian metrics for exploratory analysis. Neural Networks, 17(8–9), 1087 – 1100.
4. Liu C.-L., Yin F., Wang D.-H., Wang Q.-F. (2013). Online and offline handwritten Chinese character recognition: benchmarking on new databases. Pattern Recognition, 46(1), 155 – 162.
5. Wang D.-H., Liu C.-L., Yu J.-L., Zhou X.-D. (2009). CASIA-OLHWDB1: a database of online hand-written Chinese characters. Proceedings of the 10th International Conference on Document Analysis and Recognition (ICDAR), 1206 – 1210. Barcelona: IEEE.
6. Kollias S., Stafylopatis A., Duch W., Oja E. (Eds.), Williams B. H., Toussaint M., Storkey A. J. (2006). Extracting motion primitives from natural handwriting data. Artificial Neural Networks – ICANN 2006: Proceedings of the 16th International Conference, 634 – 643. Berlin: Springer.
7. Meier F., Theodorou E., Stulp F., Schaal S. (2011). Movement segmentation using a primitive library. Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 3407 – 3412. San Francisco: IEEE.
8. de Campos T. E., Babu B. R., Varma M. (2009). Character recognition in natural images. Proceedings of the 4th International Conference on Computer Vision Theory and Applications. Lisbon.
9. Cohen G., Afshar S., Tapson J., van Schaik A. (2017). EMNIST: an extension of MNIST to handwritten letters. CoRR, Vol. abs/1702.05373. Retrieved from https://arxiv.org/abs/1702.05373 (Accessed: 17.01.2025).
10. Llorens D. et al. (2008). The UJIPenChars data-base: a pen-based database of isolated handwritten characters. Proceedings of the International Conference on Language Resources and Evaluation – LREC 2008. Marrakech.
11. Calderara S., Prati A., Cucchiara R. (2011). Mixtures of von Mises distributions for people trajectory shape analysis. IEEE Transactions on Circuits and Systems for Video Technology, 21, 457 – 471.
12. Guyon I. M., Gunn S. R., Ben-Hur A., Dror G. (2004). Result analysis of the NIPS 2003 feature selection challenge. Advances in Neural Information Processing Systems (NIPS) 17: Proceedings of the Conference. Vancouver.
13. Lake B. M., Salakhutdinov R., Tenenbaum J. B. (2015). Humanlevel concept learning through probabilistic program induction. Science, Vol. 350 6266, 1332 – 1338.
14. LeCun Y., Bottou L., Bengio Y., Haffner P. (1998). Gradientbased learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278 – 2324.
15. Kussul E. M., Baidyk T. (2004). Improved method of handwritten digit recognition tested on MNIST database. Image and Vision Computing, 22, 971 – 981.
16. Xu L., Krzyzak A., Suen C. (1992). Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on Systems, Man and Cybernetics. Part B (Cybernetics), 22, 418 – 435.
17. Alimoglu F., Alpaydin E. (1997). Combining multiple representations and classifiers for pen-based handwritten digit recognition. Proceedings of the Fourth International Conference on Document Analysis and Recognition, 2, 637 – 640. Ulm: IEEE.
18. Tang E., Suganthan P., Yao X., Qin K. (2005). Linear dimensionality reduction using relevance weighted LDA. Pattern Recognition, 38, 485 – 493.
19. Hong Y., Li Q., Jiang J., Tu Z. (2011). Learning a mixture of sparse distance metrics for classification and dimensionality reduction. Proceedings of the 2011 International Conference on Computer Vision, 906 – 913. Barcelona: IEEE.
20. Thoma M. (2017). The Hasyv2 dataset. arXiv, Vol. abs/1701.08380. Retrieved from https://arxiv.org/abs/1701.08380 (Accessed: 17.01.2025).
21. Karki M., Liu Q., Dibiano R., Basu S., Mukhopadhyay S. (2018). Pixel-level reconstruction and classification for noisy handwritten Bangla characters. Proceedings of the 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), 511 – 516. Niagara Falls: IEEE.
22. Liu Q., Collier E., Mukhopadhyay S. (2019). PCGAN-Char: progressively trained classifier generative adversarial networks for classification of noisy handwritten Bangla characters. Proceedings of the 21st International Conference on Asian Digital Libraries (ICADL 2019). Kuching.
23. Iatsenko D., Smirnov K. (2023). Russian hand-writings tracked. Mendeley. Retrieved from https://data.mendeley.com/datasets/3h6h5d7xg2/2 (Accessed: 17.01.2025).
24. Saenko I., Lauta O., Iatsenko D. (2023). The use of dynamic characteristics in handwriting recognition tasks. 2023 International Ural Conference on Electrical Power Engineering (UralCon), 609 – 614. Chelyabinsk: IEEE.
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