https://oldena.lpnu.ua/handle/ntb/52204| Title: | Optical Character Recognition |
| Authors: | Zdebska, Tetyana Andrunyk, Vasyl Kempnyk, Roman Chyhura, Vladyslav |
| Affiliation: | Lviv Polytechnic National University |
| Bibliographic description (Ukraine): | Optical Character Recognition / Tetyana Zdebska, Vasyl Andrunyk, Roman Kempnyk, Vladyslav Chyhura // Computational linguistics and intelligent systems : proceedings of the 4nd International conference, 23-24 April 2020, Lviv, Ukraine. — Lviv : Lviv Politechnic Publishing House, 2020. — Vol 2 : Proceedings of the 4nd International conference, COLINS 2020. Workshop, Lviv, Ukraine April 23-24, 2020. — P. 90–100. — (Computational Linguistics). |
| Bibliographic description (International): | Optical Character Recognition / Tetyana Zdebska, Vasyl Andrunyk, Roman Kempnyk, Vladyslav Chyhura // Computational linguistics and intelligent systems : proceedings of the 4nd International conference, 23-24 April 2020, Lviv, Ukraine. — Lviv : Lviv Politechnic Publishing House, 2020. — Vol 2 : Proceedings of the 4nd International conference, COLINS 2020. Workshop, Lviv, Ukraine April 23-24, 2020. — P. 90–100. — (Computational Linguistics). |
| Is part of: | Computational linguistics and intelligent systems : proceedings of the 4nd International conference (2), 2020 |
| Issue Date: | 23-Apr-2020 |
| Publisher: | Видавництво Львівської політехніки Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів Lviv |
| Temporal Coverage: | 23-24 April 2020, Lviv, Ukraine |
| Number of pages: | 11 |
| Page range: | 90-100 |
| Start page: | 90 |
| End page: | 100 |
| Abstract: | Optical text recognition (OCR) programs are used to recognize text, that is, to translate from a graphic image into a text format. OCR (Optical character recognition) is a mechanical or electronic transfer saved handwritten, typewritten or printed text into a sequence of codes used to represent a text editor. Optical text recognition allows you to: - edit text; - perform search word or phrase; - store it in a more compact form; - show or print material without losing quality; - analyze information; - apply electronic translation, formatting or translation to text Optical text recognition is a research problem in the areas of pattern recognition , artificial intelligence and computer vision. Optical text recognition systems require calibration to work with a specific font ; in earlier versions, programming required the image of each character, the program could only work with one font at a time. Currently, the most common so-called "intelligent" systems that recognize most fonts with a high degree of accuracy. Some optical text recognition systems are capable of restoring the original text formatting, including images, columns, and other non-text components. |
| URI: | https://ena.lpnu.ua/handle/ntb/52204 |
| ISSN: | 2523-4013 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2020 |
| URL for reference material: | http://.wikipedia.org/wiki/Optical_Character http://en.wikipedia.org/wiki/ABBYY_FineReader http://www.ixbt.com/soft/finereader.shtml http://en.wikipedia.org/wiki/Tesseract http://mest.meste.org/MEST_1_2015/Sadrzaj_eng.html http://mest.meste.org/MEST_1_2015/5_15.pdf http://www.pan-ol.lublin.pl/wydawnictwa/Econtechmod.html |
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| References (International): | 1. Optical text recognition, http://.wikipedia.org/wiki/Optical_Character Recognition 2. FineReader, http://en.wikipedia.org/wiki/ABBYY_FineReader . 3. IXBT, http://www.ixbt.com/soft/finereader.shtml . 4. Tesseract, http://en.wikipedia.org/wiki/Tesseract . 5. ABBYY FineReader Optical Character Recognition System . Version 9.0. User's Guide. 2007 ABBYY. 6. De Campos T. E. "Character Recognition in Natural Images in the Proceedings of the International Conference on Computer Vision Theory and Applications" − Lisbon, Portugal, February − 2009. 7. Yokobayashi M. "Binarization and recognition of degraded characters using a maximum separation axis in the color space", Wakahara T. − Vol. 2nd − 2006 − pp. 885−888. 8. Pan Y. "Text localization in natural scene images based on a conditional random field in the International Conference on Document Analysis and Recognition", Hou X., Liu C, 2009. 9. 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Bakumenko, N., Strilets, V., Ugryumov, M., Application of the C-Means Fuzzy Clustering Method for the Patient's State Recognition Problems in the Medical Monitoring System. In: Computational linguistics and intelligent systems, COLINS, 218-227. (2019) 14. Dovbysh, A., Shelehov, I., Pylypenko, S., Berest, O., Estimation of Informativeness of Recognition Signs at Extreme Information Machine Learning of Knowledge Control System. In: Computational linguistics and intelligent systems, COLINS, 143-152. (2019) 15. Dovbysh, A., Alieksieiev, V., Embedding Speech Recognition Tools for Custom Software: Engines Overview. In: Computational Linguistics and Intelligent Systems, COLINS, 2, 114-121. (2018) 16. Lytvyn, V., Vysotska, V., Pukach, P., Bobyk, I., Uhryn, D., Development of a method for the recognition of authors style in the Ukrainian language texts based on linguometry, stylemetry and glottochronology. In: Eastern-European Journal of Enterprise Technologies, 4(2-88), 10-19. (2017) 17. 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Kapustiy, B.E., Rusyn, B.P., Tayanov, V.A., Peculiarities of application of statistical detection criteria for problems of pattern recognition. In: Journal of Automatioin and Inrormation Science, 37(2), 30-36. (2005) 25. Rusyn, B., Kosarevych, R., Lutsyk, O., Korniy, V., Segmentation of atmospheric clouds images obtained by remote sensing. In: 14th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET, 213-216. (2018) 26. Kapustiy, B.O., Rusyn, B.P., Tayanov, V.A., A new approach to determination of correct recognition probability of set objects. In: Upravlyaushchie Sistemy i Mashiny, 2, 8-12. (2005) 27. Rusyn, B., Prudyus, I., Ostap, V., Fingerprint image enhancement algorithm. In: The Experience of Designing and Application of CAD Systems in Microelectronics, Proceedings of the 6th International Conference ,CADSM, 193-194. (2001) 28. Rusyn, B., Torska, R., Kobasyar, M., Application of the cellular automata for obtaining pitting images during simulation process of thei growth. In: Advances in Intelligent Systems and Computing, 242, 299-306. (2014) 29. Varetskyy, Y., Rusyn, B., Molga, A., Ignatovych, A., A new method of fingerprint key protection of grid credential. In: Advances in Intelligent and Soft Computing, 84, 99-103. (2010) 30. Vysotska V. Linguistic analysis and modelling semantics of textual content for digest formation, Victoria Vysotska, Lyubomyr Chyrun, MEST Journal (Management Education Science & Society Technologie), Vol.3 No.1, PP. 127-148 [Online], ISSN 2334-7171, ISSN 2334-7058 (Online), DOI 10.12709/issn.2334-7058. This issue: DOI 10.12709/mest.02.02.02.0, Access mode: http://mest.meste.org/MEST_1_2015/Sadrzaj_eng.html http://mest.meste.org/MEST_1_2015/5_15.pdf. 31. Berko A. 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| Content type: | Article |
| Appears in Collections: | Computational linguistics and intelligent systems. – 2020 р. |
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|---|---|---|---|---|
| 2020v2_Zdebska_T-Optical_Character_Recognition_90-100.pdf | 1.83 MB | Adobe PDF | View/Open | |
| 2020v2_Zdebska_T-Optical_Character_Recognition_90-100__COVER.png | 254.14 kB | image/png | View/Open |
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