INTERPOLATSION TIKLASH ALGORITMLARINING OCR ANIQLIGIGA TA’SIRINI BAHOLASH
##plugins.pubIds.doi.readerDisplayName##:
https://doi.org/10.5281/zenodo.20125076##article.subject##:
interpolatsiya, tiklash algoritmlari, OCR, optik belgilarni tanib olish, raqamli tasvir, matn tasviri, bilinear interpolatsiya, bikubik interpolatsiya, splayn interpolatsiya, adaptiv interpolatsiya, PSNR, SSIM, belgilar aniqligi, so‘z aniqligi.##article.abstract##
Ushbu maqolada interpolatsion tiklash algoritmlarining optik belgilarni tanib olish (OCR) tizimlari aniqligiga
ta’siri baholanadi. Past aniqlikdagi, shovqinlangan yoki siqish natijasida sifati pasaygan matnli tasvirlarda OCR
natijalari ko‘pincha belgilar chegaralarining xiralashishi, harflar orasidagi masofaning buzilishi hamda mayda strukturaviy
elementlarning yo‘qolishi sababli pasayadi. Shu nuqtayi nazardan, bilinear, bikubik, splaynli va adaptiv interpolatsion
tiklash algoritmlarining matn tasvirlari sifatiga hamda OCR aniqligiga ta’siri qiyosiy tahlil qilinadi. Tadqiqotda tiklangan
tasvir sifati PSNR va SSIM metrikalari orqali, OCR samaradorligi esa belgilar aniqligi, so‘z aniqligi hamda xatolik darajasi
asosida baholanadi.
Библиографические ссылки
UNCTAD. Digital Economy Report 2024: Shaping an Environmentally Sustainable and Inclusive Digital Future. United
Nations Conference on Trade and Development (UNCTAD). – Geneva, 2024.
Smith R. An Overview of the Tesseract OCR Engine // Proceedings of the Ninth International Conference on Document
Analysis and Recognition (ICDAR). – IEEE Computer Society. – Curitiba, Brazil, 2007. – Pp. 629–633.
IDC. The Digitization of the World: From Edge to Core (Data Age 2025). International Data Corporation (IDC). –
Framingham, MA, USA, 2018.
NVIDIA. AI and Data Processing Trends: Accelerating Data-Centric Computing in the Era of Artificial Intelligence
NVIDIA Technical Report. – Santa Clara, CA, USA, 2023.
DataReportal. Digital 2025: Uzbekistan – Insights into Internet Usage, Mobile Connectivity, and Digital Adoption.
Kepios & DataReportal, 2025.
Gonzalez R.C., Woods R.E. Digital Image Processing. 4th Edition. – New York, USA: Pearson Education, 2018.
Pratt W.K. Digital Image Processing: PIKS Scientific Inside. 4th Edition. – Hoboken, NJ, USA: Wiley-Interscience,
Forsyth D.A., Ponce J. Computer Vision: A Modern Approach. 2nd Edition. – Upper Saddle River, NJ, USA: Prentice
Hall, 2012.
Smith R. An Overview of the Tesseract OCR Engine // Proceedings of the Ninth International Conference on Document
Analysis and Recognition (ICDAR). – IEEE Computer Society, 2007. – Pp. 629–633.
Shafait F., Keysers D., Breuel T.M. Efficient Implementation of Local Adaptive Thresholding Techniques Using Integral
Images // Proceedings of the International Conference on Document Analysis and Recognition (ICDAR). – IEEE,
– Pp. 136–140.
Gatos B., Pratikakis I., Perantonis S.J. Adaptive Degraded Document Image Binarization // Pattern Recognition. –
Elsevier, 2006. – Vol. 39, No. 3. – Pp. 317–327.
Otsu N. A Threshold Selection Method from Gray-Level Histograms // IEEE Transactions on Systems, Man, and
Cybernetics. – 1979. – Vol. 9, No. 1. – Pp. 62–66.
Sodiqov S.S., Malikov M.N. Tasvirlarga sonli ishlov berish asoslari. – Toshkent: “Fan” nashriyoti, 1994. – 147 bet.
Tuxtasinov M.T. Tasvirlarni dastlabki qayta ishlash algoritmlari: texnika fanlari nomzodi ilmiy darajasini olish uchun
yozilgan dissertatsiya. – Toshkent, 2007. – 120 bet.
Samandarov I.R. Tasvirlarni tahlil qilish algoritmlari va ularning qo‘llanilishi: texnika fanlari nomzodi ilmiy darajasini
olish uchun yozilgan dissertatsiya. – Toshkent, 1986. – 110 bet.
Загрузки
##submissions.published##
##issue.issue##
##section.section##
Лицензия
Copyright (c) 2026 MUHANDISLIK VA IQTISODIYOT
Это произведение доступно по лицензии Creative Commons «Attribution» («Атрибуция») 4.0 Всемирная.