{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T16:09:44Z","timestamp":1769184584717,"version":"3.49.0"},"reference-count":44,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06","funder":[{"name":"MSIT, Korea, ITRC","award":["IITP-2020-2017-0-01630"],"award-info":[{"award-number":["IITP-2020-2017-0-01630"]}]},{"name":"IITP, NRF","award":["2018R1D1A1A09084151"],"award-info":[{"award-number":["2018R1D1A1A09084151"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Wavelets Multiresolut Inf. Process."],"published-print":{"date-parts":[[2020,11]]},"abstract":"<jats:p>Methods for text detection and recognition in images of natural scenes have become an active research topic in computer vision and have obtained encouraging achievements over several benchmarks. In this paper, we introduce a robust yet simple pipeline that produces accurate and fast text detection and recognition for the Uzbek language in natural scene images using a fully convolutional network and the Tesseract OCR engine. First, the text detection step quickly predicts text in random orientations in full-color images with a single fully convolutional neural network, discarding redundant intermediate stages. Then, the text recognition step recognizes the Uzbek language, including both the Latin and Cyrillic alphabets, using a trained Tesseract OCR engine. Finally, the recognized text can be pronounced using the Uzbek language text-to-speech synthesizer. The proposed method was tested on the ICDAR 2013, ICDAR 2015 and MSRA-TD500 datasets, and it showed an advantage in efficiently detecting and recognizing text from natural scene images for assisting the visually impaired.<\/jats:p>","DOI":"10.1142\/s0219691320500526","type":"journal-article","created":{"date-parts":[[2020,8,12]],"date-time":"2020-08-12T13:46:02Z","timestamp":1597239962000},"page":"2050052","source":"Crossref","is-referenced-by-count":25,"title":["Improvement of the end-to-end scene text recognition method for \u201ctext-to-speech\u201d conversion"],"prefix":"10.1142","volume":"18","author":[{"given":"Fazliddin","family":"Makhmudov","sequence":"first","affiliation":[{"name":"Department of IT Convergence Engineering, Gachon University, Sujeong-Gu, Seongnam-Si, Gyeonggi-Do, 461-701, Korea"}]},{"given":"Mukhriddin","family":"Mukhiddinov","sequence":"additional","affiliation":[{"name":"Department of Hardware and Software of Control Systems in Telecommunications, Tashkent University of Information Technologies, named after Muhammad al-Khwarizmi, Tashkent, 100200, Uzbekistan"}]},{"given":"Akmalbek","family":"Abdusalomov","sequence":"additional","affiliation":[{"name":"Department of IT Convergence Engineering, Gachon University, Sujeong-Gu, Seongnam-Si, Gyeonggi-Do, 461-701, Korea"}]},{"given":"Kuldoshbay","family":"Avazov","sequence":"additional","affiliation":[{"name":"Department of IT Convergence Engineering, Gachon University, Sujeong-Gu, Seongnam-Si, Gyeonggi-Do, 461-701, Korea"}]},{"given":"Utkir","family":"Khamdamov","sequence":"additional","affiliation":[{"name":"Department of Hardware and Software of Control Systems in Telecommunications, Tashkent University of Information Technologies, named after Muhammad al-Khwarizmi, Tashkent, 100200, Uzbekistan"}]},{"given":"Young Im","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-Si, Gyeonggi-Do, 461-701, Korea"}]}],"member":"219","published-online":{"date-parts":[[2020,9,15]]},"reference":[{"key":"S0219691320500526BIB001","doi-asserted-by":"crossref","first-page":"3350","DOI":"10.3390\/app10103350","volume":"10","author":"Abdusalomov A.","year":"2020","journal-title":"Appl. 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