A Method for Improving the Selected Area of the Image with High-Speed Processing of Symbolic Information

The purpose of research is to find optimal methods for preparing an image with the aim of a more stable course of execution of the algorithm for detecting symbolic information.

Methods. The paper uses methods to improve the quality of images by increasing low intensity parameters, digitizing and correcting a digital image, removing noise pixels, methods of image restoration, methods of reducing chromatic aberrations.

Results. The analysis of methods for improving the quality and restoration of images is carried out; the weight parameters are found using the Gaussian filter. A method for correcting the angle of inclination of the original image is presented; a table of the probability of detecting and recognizing symbolic information after applying the methods of improving the selected area of the image for high-speed processing of symbolic information is presented.

Conclusion. In the course of the work, it was revealed that with a sufficient image resolution, the use of methods for improving the selected area allows with a probability of up to 95% to detect and recognize symbolic information at a speed acceptable for a high-speed device (up to 280 ms), depending on the quality of the original image and the number of detected areas containing symbolic information

1. Panishchev V. S., Trufanov M. I., Dobroserdov O. G., Homyakov O. O. Raspoznavanie simvol'noj informacii dlya avtomatizacii proizvodstvennyh processov [Recognition of symbolic information for the automation of production processes]. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta = Proceedings of the Southwest State University, 2021, no. 25 (1), pp. 122-137.

2. Hapiro S. L., Stokman. Komp'yuternoe zrenie [Computer vision]. Moscow, Binom Publ., 2006. 762 p.

3. Trufanov M. I., Panishchev V. S., Frolov M. M. [Technical vision system with multiple sources of images]. Informacionnye tekhnologii i matematicheskoe modelirovanie sistem 2020. Trudy mezhdunarodnoj nauchno-tekhnicheskoj konferencii [Information Technologies and mathematical modeling of Systems 2020. Proceedings of the International Scientific and Technical Conference]. Odintsovo, Center for Information Technologies in Design of the Russian Academy of Sciences Publ., 2020, pp. 124-126. (In Russ.)

4. Konanykhin A. U., Panishchev V. S. [Algorithm for high-speed processing of symbolic information]. Sbornik nauchnykh statei VIII Mezhdunarodnoi nauchno-prakticheskoi konferentsii IIS-2020 [Collection of scientific articles of the VIII International Scientific and Practical Conference of the IIS-2020]. Kursk, Southwest State University Publ., 2020, pp. 7-9. (In Russ.)

5. Volkov D. A., Panishchev V. S., Trufanov M. I. Metod korrekcii distorsii v zadachah obrabotki izobrazhenij etiketok [Distortion Correction Method for Label Image Processing]. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta = Proceeding of the Southwest State University, 2019, vol. 23, no. 3, pp. 135-147.

6. Burmaka A. A., Govoruhina T. N., Razumova K. V. Sistemnaya model' raspoznavaniya i identifikacii sostoyaniya predstatel'noj zhelezy po ee izobrazheniyam UZI I TRUZI [A systemic model for recognizing and identifying the state of the prostate gland by its images USS AND TRUS]. Naukoemkie tekhnologii = Science-Intensive Technologies, 2014, vol. 15, no. 12, pp. 13-20.

7. Burmaka A. A., Govoruhina T. N., Terekhova O. A. Mnogokanal'naya izmeritel'naya sistema s nechetkim opisaniem eyo sostoyanij [Multichannel measuring system with a fuzzy description of its states]. Biomedicinskaya radioelektronika = Biomedical Radioelectronics, 2014, no. 9, pp. 28-31.

8. Korenevskij N. A., Yuldashev Z. M., Konanyhina T. N. Matematicheskie metody obrabotki mediko-biologicheskoj informacii. Matematicheskaya statistika [Mathematical methods for processing biomedical information. Math statistics]. Staryj Oskol, Novye naukoemkie tekhnoloogii Publ., 2021. 304 p.

9. Konanykhin A. Y., Konanykhina T. N. Algorithm for finding contours for the purpose of processing symbolic information in conditions of small symbol size and chromatic aberrations. Conference Series. 10. Ser. "X International Scientific and Practical Conference Information and Measuring Equipment and Technologies. IMET 2020, 2021. Top Publishing, 2021,

10. p. 012018.

11. Konanyhina T. N., Lisicin L. A., Bobyncev D. O., Abramova T. V. Sistemnyj analiz signalov v informacionno-izmeritel'nyh sistemah raspredelennogo tipa [System analysis of signals in information-measuring systems of a distributed type]. Kursk, Universitetskaya kniga Publ., 2021. 141 p.

12. Vasin D., Ershov M. [Character recognition based on low-level graphic image description models]. Grafikon-2014. Trudy XXIV Mezhdunarodnoi konferentsii po komp'yuternoi grafike i zreniyu [Graphicon-2014. Proceedings of the XXIV International Conference on Computer Graphics and Vision]. Moscow, Graphics Publ., 2014, pp. 62-64. (In Russ.)

13. Potapov A. Sistemy komp'yuternogo zreniya: sovremennye zadachi i metody [Computer vision systems: modern tasks and methods]. Control Engineering, Russia, 2014, no. 1, pp. 20-26.

14. Ishmam Zabir, Sudip Paul, Md. Abu Rayhan, Tanmoy Sarker, Shaikh Anowarul Fat- tah, Celia Shahnaz. Automatic Brain Tumor Detection and Segmentation from Multi-Modal MRI Images Based on Region Growing and Level Set Evolution. IEEE International WIE Conference on Electrical and Computer Engineering, 2015, pp. 503-506.

15. Hasan A. A., Panishchev V. S., Reshetnikova V. P. Analiz teksturnyh priznakov na izobrazheniyah [Analysis of texture features in images]. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Seriya: Upravlenie,vychislitel'naya tekhnika, informatika. Meditsinskoe priborostroenie = Proceedings of the Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering, 2014, no. 2, pp. 102107.

16. Mishin A. B., Panishchev V. S., Tkachev P. Yu. Vybor struktury nejrosetevogo fil'tra dlya obrabotki cifrovyh izobrazhenij [Choosing a neural network filter structure for digital image processing]. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta = Proceeding of the Southwest State University, 2013, no. 3 (48), pp. 14-18.

17. Hasan A. A. A., Panishchev V. S., Trufanov M. I. Algoritm sravneniya izobrazhenij na osnove analiza statisticheskih harakteristik tekstury [Algorithm for comparing images based on the analysis of statistical characteristics of the texture]. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Seriya: Upravlenie,vychislitel'naya tekhnika, informatika. Meditsinskoe priborostroenie = Proceedings of the Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering, 2017, vol. 7, no. 4 (25), pp. 34-40.

18. Bulaev M. I., Panishchev V. S., Reshetnikova V. P., Trufanov M. I. Modelirovanie nejroseti dlya podavleniya shumov na izobrazhenii [Modeling a neural network to suppress image noise]. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta = Proceedings of the Southwest State University, 2015, no. 6 (63), pp. 58-62.

19. Panishchev V. S., Homyakov O. O. [Recognition of symbolic information on the labeling of circuit breakers]. Informacionnye tekhnologii i matematicheskoe modelirovanie sis- tem 2020. Trudy mezhdunarodnoj nauchno-tekhnicheskoj konferencii [Information Technologies and mathematical modeling of Systems 2020. Proceedings of the International Scientific and Technical Conference]. Odintsovo, Center for Information Technologies in Design of the Russian Academy of Sciences Publ., 2019, pp. 106-109. (In Russ.)

20. Panishchev V. S., Bysov I. D. [A neural network for handwriting recognition]. Optiko-elektronnye pribory i ustrojstva v sistemah raspoznavaniya obrazov, obrabotki izobrazhenij i simvol'noj informacii. Raspoznavanie - 2018. Sbornik materialov XIV Mezhdunarodnoj nauchno-tekhnicheskoj konferencii [Optoelectronic devices and devices in image recognition systems, image processing and symbolic information. Recognition - 2018. Collection of materials of the XIV International scientific and technical conference]. Kursk, Southwest State University Publ., 2018, pp. 205-206. (In Russ.)

21. Galeev D. T., Miroshnichenko S. Yu., Panishchev V. S. Razrabotka iskusstvennoj nejronnoj seti dlya resheniya zadachi interpolyacii izobrazhenij [Development of an artificial neural network for solving the problem of image interpolation]. Telekommunikacii = Telecommunications, 2021, no. 3, pp. 11-16.

22. Panishchev V. S. Algoritm vosstanovleniya pikselej izobrazheniya na osnove nejronnoj seti [Algorithm for recovering image pixels based on a neural network]. Izvestiya Yugo- Zapadnogo gosudarstvennogo universiteta = Proceeding of the Southwest State University, 2018, vol. 22, no. 6 (81), pp. 183-188.