PROCESSING IMAGES OF SALES RECEIPTS FOR ISOLATING AND RECOGNISING TEXT INFORMATION
https://doi.org/10.21822/2073-6185-2019-46-4-113-122
Abstract
Objectives. This article presents an application for the processing of scanned images of sales receipts for subsequent extraction of text information using the Tesseract OCR Engine. Such an application is useful for maintaining a family budget or for accounting in small companies. The main problem of receipt recognition is the low quality of ink and printing paper, which results in creasing and tears, as well as the rapid fading of printed characters.
Methods. The study is based on a number of algorithms based on mathematical morphology methods for opening, closing and morphological gradient operations, as well as image conversion, which can significantly improve the final recognition of characters by Tesseract.
Results. In order to solve this problem, a special image normalisation algorithm is proposed, which includes locating a receipt on an image, processing the received image section, removing image capture and carrier defects, as well as point processing for restoring missing characters. The developed application supports increased recognition accuracy of text information when using Tesseract OCR.
Conclusion. The developed system recognises characters with fairly high accuracy, demonstrates a result that is better than that obtained when using the unmodified Tesseract method, but which is still inferior to the recognition accuracy of ABBY FineReader. Methods are also been proposed aimed at improving the developed algorithm.
About the Authors
A. S. Nazdryukhin
Polzunov Altai State Technical University
Russian Federation
Russian Federation
Student,
46 Lenin Ave., Barnaul 656038
I. N. Khramtsov
Polzunov Altai State Technical University
Russian Federation
Russian Federation
Student,
46 Lenin Ave., Barnaul 656038
A. N. Tushev
Polzunov Altai State Technical University
Russian Federation
Russian Federation
Cand. Sci. (Technical), Assoc. Prof., Department of Informatics, Computer Engineering and Information Security,
46 Lenin Ave., Barnaul 656038
References
1. ABBYY FineReader Homepage, https://www.abbyy.com/en-us/finereader/
2. Tesseract Open Source OCR Engine, https://github.com/tesseract-ocr/tesseract
3. OpenCV Homepage, https://opencv.org/
4. Gonzalez R., Woods, R.: Digital Image Processing, 4th edn. Pearson, New York (2018).
5. Bradley D., Roth, G.: Adaptive Thresholding using the Integral Image. J. Graphics Tools 12, 13-21 (2007).
6. Suzuki S., Keiichi A. Be.: Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing, 30(1), 32–46 (1985).
7. Sklansky J.: Finding the Convex Hull of a Simple Polygon. Pattern Recognition Letters 1(2), 79-83 (1982).
8. Sencar H.T., Memon, N.: Digital image forensics: There is more to a picture than meets the eye. Springer, New York (2013).
9. Chambolle A., Caselles V., Novaga M., Cremers D., Pock, T.: An introduction to Total Variation for Image Analysis 9 (2010).
10. Ma Z. & Wen, J.:Single-scale Retinex sea fog removal algorithm fused the edge information. Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics 27. 217-225 (2015).
11. Li, B., Xu, D., Lee, M., Feng, S.: A Multi-Scale Adaptive Grey World Algorithm. IEICE Transactions 90-D, 1121-1124 (2007).
12. Babakhani R., Zarei P.: Automatic gamma correction based on average of brightness. Advances in Computer Science: an International Journal 4(6), 156-159 (2015).
13. Rahman, Sh., Rahman, Md. Mostafijur, Abdullah-Al-Wadud, M., Al-Quaderi, Golam Dastegir, Shoyaib, M.: An adaptive gamma correction for image enhancement. EURASIP Journal on Image and Video Processing 35, (2016).
14. Haralick R., Shapiro, L.: Computer and Robot Vision, 1st edn, Addison-Wesley Publishing Company, Boston (1992).
15. Marr D., Hildreth, E.: Theory of Edge Detection. Proceedings of the Royal Society of London B 207, 187- 217 (1980)
16. Pelevin Ye.Ye., Balyasnyy S.V. Ispol'zovaniye metoda Adaptive Threshold v sisteme tekhniche-skogo zreniya // Juvenis Scientia. 2017. №1. S. 4-7. [Pelevin, E., Balyasny, S.: The usage of adaptive threshold method in the system of computer vision. Juvenis Scientia 1, 4-7 (2017). (In Russ)]
17. A.Rosebrock, Practical Python and OpenCV, 3rd edn. PyImageSearch.com (2016).
18. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. Sixth International Conference on Computer Vision, 839-846 (1998).
19. Peter Norvig’s cite: How to write a spelling corrector, https://norvig.com/spell-correct.html, last accessed 2019/11/30.
20. Liu, X., Cheng, K., Luo, Y., Duh, K., & Matsumoto, Y.: A hybrid Chinese spelling correction using language model and statistical machine translation with reranking. In Proceedings of the Seventh SIGHAN Workshop on Chinese Language Processing, 54-58 (2013).
21. Popov, V., Kudinov, M., Piontkovskaya, I., Vytovtov, P., Nevidomsky, A.: Differentially Private Distributed Learning for Language Modeling Tasks (2017), https://arxiv.org/abs/1712.07473
Review
For citations:
Nazdryukhin A.S., Khramtsov I.N., Tushev A.N. PROCESSING IMAGES OF SALES RECEIPTS FOR ISOLATING AND RECOGNISING TEXT INFORMATION. Herald of Dagestan State Technical University. Technical Sciences. 2019;46(4):113-122. (In Russ.) https://doi.org/10.21822/2073-6185-2019-46-4-113-122
Views:
868
Email this article 
