O‘ZBEK IMO-ISHORA TILI UCHUN DASTLABKI MA’LUMOTLAR BAZASINI SHAKLLANTIRISH VA SUN’IY INTELLEKT ASOSIDA TANIB OLISH MODELINI ISHLAB CHIQISH
##submission.downloads##
Ushbu tadqiqotda O‘zbekiston Respublikasida eshitish va nutqida nuqsoni bo‘lgan shaxslarning raqamli inkluziyasini ta’minlash maqsadida o‘zbek imo-ishora tili uchun dastlabki ma’lumotlar bazasi (dataset) shakllantirish va sun’iy intellekt asosida tanib olish modelini ishlab chiqish masalalari yoritilgan. Tadqiqot dolzarbligi Prezidentimiz Sh.M. Mirziyoyevning “Raqamli O‘zbekiston – 2030” strategiyasi va nogironligi bo‘lgan shaxslarni qo‘llab-quvvatlash bo‘yicha davlat dasturlaridan kelib chiqadi. Metodologiya sifatida qo‘l landmarklarini MediaPipe orqali ekstraksiya qilish, vaqt seriyali ma’lumotlarni LSTM arxitekturasi bilan qayta ishlash va frameworklar taqqoslash usullari qo‘llanildi. Natijada 30 nafar ishtirokchidan 50 ta imo-ishora uchun 15,000 video yozuv yig’ildi, model test bosqichida 80.7% aniqlik ko‘rsatdi. Tadqiqotning ilmiy yangiligi o‘zbek imo-ishora tili xususiyatlarini hisobga olgan birinchi ochiq dataset prototipini yaratishda, amaliy ahamiyati esa kelgusida real vaqt rejimida ishlovchi surdo-tarjimon tizimini yaratish imkoniyatida namoyon bo‘ladi.
1. Mirziyoyev Sh.M. “Raqamli O‘zbekiston – 2030” strategiyasi. O‘zbekiston Respublikasi Prezidenti Farmoni PF-60-son, 2020.
2. Stoll S., et al. “Sign language recognition: A deep survey.” Expert Systems with Applications, vol. 164, 2021, pp. 113794.
3. Koller O., et al. “Deep continuous sign language recognition.” CVPR Workshops, 2015, pp. 53–61.
4. Moryossef A., et al. “How2Sign: A large-scale multimodal dataset for continuous sign language recognition.” CVPR, 2021, pp. 2735–2744.
5. Hochreiter S., Schmidhuber J. “Long Short-Term Memory.” Neural Computation, vol. 9, no. 8, 1997, pp. 1735–1780.
6. Cui R., Liu H., Zhang C. “A deep neural framework for continuous sign language recognition by iterative training.” IEEE Transactions on Multimedia, vol. 21, no. 7, 2019, pp. 1880–1891.
7. Vaswani A., et al. “Attention Is All You Need.” Advances in Neural Information Processing Systems (NeurIPS), vol. 30, 2017.
8. Lugaresi C., et al. “MediaPipe: A Framework for Building Perception Pipelines.” arXiv preprint arXiv:1906.08172, 2019.
9. Karimov A., Rajabov O. “O‘zbek imo-ishora alifbosi uchun dastlabki tasniflash modeli.” O‘zbekiston fanlar akademiyasi axborotnomasi, 2022, №3, 45–52-betlar.
10. Li D., et al. “MS-ASL: A Large-Scale Dataset for American Sign Language Recognition.” IEEE FG, 2019, pp. 1–7.
Mulkiiyat (c) 2026 «O‘zMU XABARLARI»
Ushbu ish quyidagi litsenziya asosida ruxsatlangan Kreativ Commons Attribution-NonCommercial-ShareAlike 4.0 International litsenziyasi asosida bu ish ruxsatlangan..
.jpg)
.png)
