| DC Field | Value | Language |
|---|
| dc.contributor.author | Liu, J. H. | - |
| dc.contributor.author | Xiong, S. | - |
| dc.contributor.author | Dang, Z. F. | - |
| dc.contributor.author | Ma, J. | - |
| dc.coverage.spatial | Минск | en_US |
| dc.date.accessioned | 2025-07-02T10:30:47Z | - |
| dc.date.available | 2025-07-02T10:30:47Z | - |
| dc.date.issued | 2025 | - |
| dc.identifier.citation | Liu, J. H. Russian handwriting recognition based on deep learning / J. H. Liu, S. Xiong, Z. F. Dang, J. Ma // Технологии передачи и обработки информации : материалы Международного научно-технического семинара, Минск, апрель 2025 г. / Белорусский государственный университет информатики и радиоэлектроники ; редкол.: В. Ю. Цветков [и др.]. – Минск, 2025. – С. 112–114. | en_US |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/60669 | - |
| dc.description.abstract | This paper presents a deep learning-based algorithm for Russian handwriting recognition. The algorithm combines the improved VGG network feature extraction capability and LSTM temporal modeling capability and introduces data enhancement and optimizer tuning strategies. Experimental results show that the algorithm provides significant improvement in recognition accuracy and training efficiency compared with existing methods. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | БГУИР | en_US |
| dc.subject | материалы конференций | en_US |
| dc.subject | deep learning | en_US |
| dc.subject | text recognition | en_US |
| dc.subject | russian language | en_US |
| dc.title | Russian handwriting recognition based on deep learning | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | Технологии передачи и обработки информации : материалы Международного научно-технического семинара (2025)
|
