Abstract
Segmentation is an important step within optical character recognition systems, since the recognition rates depends strongly on the accuracy of binarization techniques. Hence, it is necessary to evaluate different segmentation methods for selecting the most adequate for a specific application. However, when gold patterns are not available for comparing the binarized outputs, the recognition rates of the entire system could be used for assessing the performance. In this article we present the evaluation of five local adaptive binarization methods for digit recognition in water meters by measuring misclassification rates. These methods were studied due to of their simplicity to be implemented in based-camera devices, such as cell phones, with limited hardware capabilities. The obtained results pointed out that Bernsens method achieved the best recognition rates when the normalized central moments are employed as features.
Chapter PDF
Similar content being viewed by others
Keywords
References
Anagnostopoulos, C.N.E., Anagnostopoulos, I.E., Loumos, V., Kayafas, E.: A License Plate-Recognition Algorithm for Intelligent Transportation System Applications. IEEE Transactions on Intelligent Transportation Systems, 377–392 (2006)
Ji-yin, Z., Rui-rui, Z., Min, L., Yin, L.: License Plate Recognition Based on Genetic Algorithm. In: International Conference on Computer Science and Software Engineering, pp. 965–968 (2008)
Coughlan, J.H.S.: Reading lcd led displays with a camera cell phone. In: Conference on Computer Vision and Pattern Recognition Workshop (CVPRW 2006), pp. 119–119. IEEE Computer Society, Washington, DC, USA (2006)
Tian, L., Kamata, S.: An iterative image enhancement algorithm and a new evaluation framework. In: IEEE International Symposium on Industrial Electronics (ISIE 2008), pp. 992–997 (2008)
Thulke, M., Margner, V., Dengel, A.: Quality evaluation of document segmentation results. In: Proceedings of the Fifth International Conference on Document Analysis and Recognition, ICDAR 1999 (1999)
Trier, O.D., Jain, A.K.: Goal-directed evaluation of binarization methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1191–1201 (1995)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice-Hall, NewJersey (2002)
J. Bernsen.: Dynamic thresholding of gray-level images. In: Proc. Eighth Int. Conf. Pattern Recognition, pp. 1251–1255 (1986)
Niblack, W.: An Introduction to Digital Image Processing, pp. 115–116. Prentice Hall, Englewood Cliffs (1986)
Sauvola, J., Pietikinen, M.: Adaptive document image binarization. Pattern Recognition 33(2), 225–236 (2000)
Wellner, P. D.: Adaptive Thresholding for the DigitalDesk. Technical Report EPC-1993-110, Rank Xerox Ltd. (1993)
White, J.M., Rohrer, G.D.: Image thresholding for optical character recognition and other applications requiring character image extraction. IBMJ. Research and Development 27(4), 400–411 (1983)
Rais, N.B., Hanif, M.S., Taj, I.A.: Adaptive thresholding technique for document image analysis. In: Proc: 8th International Multitopic Conference, pp. 61–66 (2004)
Huang, Z., Leng, J.: Analysis of hu’s moment invariants on image scaling and rotation. In: 2nd International Conference on Computer Engineering and Technology (ICCET 2010), vol. 7, pp. 476–480. IEEE Computer Society, Los Alamitos (2010)
Liu, H., Motoda, H.: Computational Methods of Feature Selection. Taylor & Francis, Boca Raton (2008)
Gómez, W., Leija, L., Díaz-Pérez, A.: Mutual Information and Intrinsic Dimensionality for Feature Selection. In: 7th International Conference on Electrical Engineering,Computing Sciences and Automatic Control (CCE 2010), Tuxtla Gutiérrez, Chiapas, September 8–10, pp. 339–344 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nava-Ortiz, M., Gómez-Flores, W., Díaz-Pérez, A., Toscano-Pulido, G. (2011). Evaluation of Binarization Algorithms for Camera-Based Devices. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Ben-Youssef Brants, C., Hancock, E.R. (eds) Pattern Recognition. MCPR 2011. Lecture Notes in Computer Science, vol 6718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21587-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-21587-2_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21586-5
Online ISBN: 978-3-642-21587-2
eBook Packages: Computer ScienceComputer Science (R0)