Article Review Procedure
Academic Areas and Subjects
Applied Aspects of Information Technology
Search by article
Vol. 4 № 1
Vol. 3 № 1
Vol. 3 № 2
Vol. 3 № 3
Vol. 3 № 4
Vol. 2 № 1
Vol. 2 № 2
Vol. 2 № 3
Vol. 2 № 4
Vol. 1 № 1
18 Feb 2021
26 Feb 2020
Informatics, Culture and Technology
20 May 2019
Informatics, Culture and Technology
THE TECHNIQUE OF EXTRACTION TEXT AREAS ON SCANNED DOCUMENT IMAGE USING LINEAR FILTRATION
The method of selection of text areas on the image of the scanned document from the background is proposed. Text areas of the image have approximately the same intensity values inside these areas. Therefore, linear filtering and threshold image transformation are used. Linear filtering allows you to smooth out the intensity values of pixels inside homogeneous areas. In the case of a threshold transformation, the threshold value is used, which makes it possible to isolate homogeneous areas of the im-age that make up the text fragments from the background.A study was conducted on the selection of a threshold value for highlight-ing homogeneous areas of text, which showed that the threshold value is better to choose among the pixel intensities at the base of the histogram peak, which corresponds to the background. It is proposed to select the threshold by the value of the second derivative for the image histogram after linear filtering. Therefore, the intensity of the local maximum of the histogram, which is closer than the other local maxima to the right end of the image intensity interval, is chosen as the threshold. For this purpose, an analysis of the histogram of the distribution of image pixel intensity values is carried out after linear filtering by rows and columns at each step. Testing of the proposed method of separating textual image areas was carried out for segmentation of textual images of scanned archival newspapers from the MediaTeam documents database at the University of Oulu (Finland).The proposed method of extract-ing text fragments from the background using linear filtering and threshold conversion allowed to improve the quality of selection of these areas compared to the similar method in the percentage of correct recognition of text areas by 12 %, which is important for the task of image segmentation.
Alexandr G. Nesteryuk
, Candidate of Technical Sciences, Associate Professor, Department of Computer Systems
( email@example.com )
, Senior Lecturer of Department of Applied Mathematics and Information Technologies
( firstname.lastname@example.org )
Marina V. Polyakova
, Doctor of Technical Sciences, Associate Professor, Department of Applied Mathematics and Information Technologies
( email@example.com )
image segmentation; text areas; scanned document; linear filtering; image processing
1. Antonacopoulos, A., Gatos, B., & Bridson, D. (2005). “ICDAR 2005 page segmentation compe-tition,” in Proc. ICDAR, Seoul, Korea, pp. 75-80.
2. Sasirekha, D., & Chandra, Dr. E. (2012). “Enhanced Techniques for PDF Image Segmentation and Text Extraction”, International Journal of Elec-tronics and Computer Science Engineering, pp. 1833-1839.
3. Gupta, N., & Banga, V. K. (2012). “Image Segmentation for Text Extraction”, 2nd Interna-tional Conference on Electrical, Electronics and Civil Engineering (ICEECE'2012), Singapore, 28-29 April, 2012, pp. 182-185.
4. (2007). Kumar, S., Gupta, R., Khanna, N. [et al.] “Text Extraction and Document Image Segmen-tation Using Matched Wavelets and MRF Model”, IEEE Transactions on Image Processing, Vol. 16, No. 8. pp. 2117-2128.Doi: 10.1109/tip.2007.900098.
5. Wong, K. Y., Casey, R. G., & Wahl, F. M. (1982). Document analysis system. IBM Journal of Research and Development, Vol. 26(6), pp. 647-656. Doi: 10.1147/rd.266.0647.
6. Esposito, F., Malerba, D., & Semeraro, G. (1995). “A knowledge-based approach to the layout analysis”, Proceedings of the 3rd International Con-ference on Document Analysis and Recognition, Vol. 1, pp. 466-471. Doi: 10.1109/ICDAR.1995.599037.
7. Li, L., Yu, S., Zhong, L., & Li, X. (2015). “Multilingual text detection with nonlinear neural network”. Mathematical Problems in Engineering, Vol. 2015, 431608 (7 p.).Doi: 10.1155/2015/431608.
8. (2011). Erkilinc, M. S., Jaber, M., Saber E. [et al.]. “Analysis and classification for complex scanned document”, SPIE Newsroom. Doi: 10.1117/2.1201107.003819.
9. Mathur, G. Ms., & Rikhari, S. (2017). “Text Detection in Document Images: Highlight on using FAST algorithm”, International Journal of Ad-vanced Engineering Research and Scienc, Vol. 4, No. 3, pp. 275-284. Doi: 10.22161/ijaers.4.3.43.
10. Shafait, F., Keysers, D., & Breuel, T. M. (2008). “Performance Evaluation and Benchmarking of SixPage Segmentation Algorithms Pattern Analy-sis and Machine”, Intelligence, IEEE Transactions on, Vol. 30, pp. 941-954.
11. Bukhari, S. S., Shafait F., & Breuel, T. (2011). “Improved document image segmentation algorithm using multiresolution morphology” [Text], In Proc. of the 18th Document Recognition and Re-trieval Conf., Document Recognition and Retrieval XVIII – DRR 2011. San Jose, CA, USA, 2011. Doi: 10.1117/12.873461.
12. Zirari, F., Ennaji, A., Nicolas, S., & Mammass, D. (2013). “A document image segmen-tation system using analysis of connected compo-nents”, Proceeding of the 12th Int. Conf. ICDAR 2013 (Document Analysis and Recognition). Wash-ington. DC. USA, 753–757. Doi: 10.1109/icdar.2013.154
13. Smith, R. W. (2013). “History of the Tes-seract OCR engine: what worked and what didn’t”, Proceedings of SPIE, Vol. 8658, 865802.
14. Das A. K., & B. ChandaD, (1998), “Seg-mentation of text and graphics in document image: A morphological approach”, in Proc. Inf. Conf. Computational Linguistics, Speech and Document Processing, Calcutta, India, Dec., pp. A50-A56.
15. Vil’kin, A. M., Safonov, I. V., & Egorova, M. A. (2011). “Algorithm for page segmentation”, Digital Signal Processing and its Applications.
16. Rege, P. P., & Chandrakar, C. A. (2012). “Text-Image Separation in Document Images Using Boundary”, Perimeter Detection, ACEEE Interna-tional Journal on Signal & Image Processing. Vol. 4, No. 1, pp. 10-14. Doi: 01.ijsip.03.01.70.
17. Skvortsov, A. V. (2002). Triangulyatsiya Delone i yeyo primeneniye. [Delaunay triangulation and its application], Tomsk, Russian Federation, Izd-vo Tomskogo un-ta,128 p. ISBN: 5-7511-1501-5 (in Russian).
18. K. Kise, A. Sato, & M. Iwata. (1998). “Segmentation of page images using the area Voro-noi diagram”, Computer Vision and Image Under-standing, Vol. 70, Issue 3, pp. 370-382. Doi: 10.1006/cviu.1998.0684.
19. Ishchenko, А., Polyakova, M., Kuvaieva, V., & Nesteryuk, A. (2018). “Elaboration of structural representation of regions of scanned document im-ages for MRC model”, Eastern-European Journal of Enterprise Technologies, No. 6/2 (96), pp. 32-38. Doi: 10.15587/1729-4061.2018.147671.
20. Polyakova, M., Ishchenko, A., & Huliaieva, N. (2018), “Document image segmentation using averaging filtering and mathematical morphology”, 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET), Lviv-Slavske, Ukraine, pp. 966-969.
21. Haralick, R. M. (1979). “Statistical and structural approaches to texture”, Proceedings of the IEEE, Vol. 67, No. , pp. 786-804.
22. Gonsales, R. S., Vuds, R. E., & Eddins, S. L. (2006), “Cyfrovaya obrabotka izobragenij v srede Matlab”. [Digital image processing using MATLAB], Moscow, Russian Federation, Tehnosfera, 616 p. (in Russian).
23. Otsu, N. (1979). “A threshold selection method from gray-level histograms” [Text], IEEE Trans. Syst. Man, Cybern. V. SMC-9. pp. 62-66. Doi: 10.1109/tsmc.1979.4310076.
24. Gonsales, R., & Vuds, R. (2005). “Cyfrovaja obrabotka izobragenij”. [Digital image processing], Мoscow, Russian Federation, Тechnosfera, 1072 p. (in Russian)
25. Sauvola, J., & Kauniskangas, H. (1999). “MediaTeam Document Database II”. [Electronic resource]. A collection of document images, University of Oulu. Finland. (CD-R).
26. (2019). “Confusion_matrix” [Electronic resource]. Access mode: http://en.wikipedia.org/wiki/Confusion – Active link: – 20.06.2019.
Vol. 2 № 3, 2019
11 May 2021
Search by author
Information Systems and Technologies
1. Models and Methods of Information Technology
2. Design of Information Systems and Technologies
3. Mathematical Issues of Information Technologies
4. Innovative Technologies in Education, Culture and art
5. Game Technologies, Augmented and Virtual Reality
6. Theoretical and Applied Issues of Computer Science
7. Project, Program and Portfolio Management
Digital control of Technical and Social Systems
1. Adaptive and optimal Control Systems
2. Parametric and System Identification
3. Interconnected Systems and Systems with Distributed Parameter
4. Renewable Energy Systems
5. Machine Learning and Artificial Intelligence in General Technical Problems and Electromechanics
6. Management of Production and Power Plants
7. Control Systems for Robotic Systems and Complexes, Electric Vehicles
8. Diagnosis and Evaluation of Complex Systems
9. Simulation of Physical Objects and Processes
Sensor less Control Systems
Software Engineering and Systems Analysis
1. Methods and Means of Intellectual Information Processing
2. Recognition, Decision Making, Forecasting
3. Neural Network Technologies and Machine Learning Methods
4. Semantic Models. Natural Language Processing
5. Theoretical and Applied Issues of Software Engineering
6. Models and Methods of Software Quality Management
Computer Systems and Cybersecurity
1. Parallel and Distributed Information Processing
2. Internet of Things
3. Information Security and Cybersecurity
4. Computer Networks and Systems
5. Components of Robotic Systems
KarelWintersky ] [
[ © Odessa National Polytechnic University, 2018.]