| DC Field | Value | Language |
|---|
| dc.contributor.author | Liu, J. H. | - |
| dc.contributor.author | Xiong, S. | - |
| dc.contributor.author | Dang, Z. F. | - |
| dc.coverage.spatial | Минск | en_US |
| dc.date.accessioned | 2025-09-09T09:01:40Z | - |
| dc.date.available | 2025-09-09T09:01:40Z | - |
| dc.date.issued | 2025 | - |
| dc.identifier.citation | Liu, J. H. Deep learning based russian handwriten recognition / J. H. Liu, S. Xiong, Z. F. Dang // Информационная безопасность : сборник материалов 61-й научной конференции аспирантов, магистрантов и студентов БГУИР, Минск, 21–25 апреля 2025 г. / Белорусский государственный университет информатики и радиоэлектроники. – Минск, 2025. – С. 84–87. | en_US |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/61494 | - |
| dc.description.abstract | This paper presents a Russian handwriting recognition algorithm based on deep learning. The algorithm combines improved VGG
network feature extraction capabilities with LSTM time modeling capabilities and introduces data enhancement and optimizer tuning strategies.
Experimental results show that the proposed algorithm is significantly superior to the existing methods in recognition accuracy and training
efficiency. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | БГУИР | en_US |
| dc.subject | материалы конференций | en_US |
| dc.subject | russian handwriting recognition | en_US |
| dc.subject | deep learning | en_US |
| dc.subject | VGG network | en_US |
| dc.title | Deep learning based russian handwriten recognition | en_US |
| Appears in Collections: | Информационная безопасность : материалы 61-й научной конференции аспирантов, магистрантов и студентов (2025)
|
