Abstract
Text Binarization from scene images plays a crucial task for any text segmentation scheme and therefore in the OCR performance. So, an effective image binarization method is required for text segmentation and recognition tasks. This work describes an effective image binarization scheme for segmentation and recognition task of text from images. To binarize the image, Canny’s edge information is incorporated into Otsu’s method. It generates numerous components which are analyzed for segmentation of probable text components. Further, a few features are considered for classification of text and non-text. For this problem, SVM is considered. For training SVM classifier, information from ground-truth images of text and our own made non-text components are used. Finally, Multilayer Perceptron (MLP) is used for recognition of the text symbols. The MLP classifier is trained using 13496 samples of 39 classes. We tested our schemes on the publicly available ICDAR Born Digital data set. The outcomes are quite acceptable.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 377–393 (1979)
Niblack, W.: An Introduction to Digital Image Processing. Prentice Hall, Englewood Cliffs (1986)
Sauvola, J., Pietikinen, M.: Adaptive document image binarization. Pattern Recognit. 2, 225–236 (2000)
Gatos, B., Pratikakis, I., Perantonis, S.J.: Document image binarization by using a combination of multiple binarization techniques and adapted edge information. In: Proceedings of the International Conference on Pattern Recognition (ICPR) (2008)
Ghoshal, R., Roy, A., Banerjee, A., Dhara, B., Parui, S.: A novel method for binarization of scene text images and its application in text identification. Pattern Anal. Appl. 1–15 (2018)
Ghoshal, R., Roy, A., Bhowmik, T.K., Parui, S.K.: Decision tree based recognition of Bangla text from outdoor scene images. In: Eighteen International Conference on Neural Information Processing (ICONIP), pp. 538–546 (2011)
Bhattacharya, U., Shridhar, M., Parui, S.K.: On recognition of handwritten Bangla characters. In: Proceedings of the Conference on Computer Vision, Graphics and Image Processing (ICVGIP), pp. 817–828 (2006)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. Institute for cognitive science report 8506. San Diego: University of California (1985)
Karatzas, D., Robles Mestre, S., Mas, J., Nourbakhsh, F., Roy, P.P.: ICDAR 2011 robust reading competition-challenge 1: reading text in born-digital images (web and email). In: Proceedings of the 11th International Conference of Document Analysis and Recognition (ICDAR), pp. 1485–1490 (2011)
Dance, C.R., Seegar, M.: On the evaluation of document analysis components by recall, precision, and accuracy. In: Proceedings of the Fifth International Conference on Document Analysis and Recognition (ICDAR), pp. 713–716 (1999)
Bhattacharya, U., Parui, S.K., Mondal, S.: Devanagari and bangla text extraction from natural scene images. In: Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), pp. 171–175 (2009)
Kumar, D., Ramakrishnan, A.G.: OTCYMIST:Otsu-Canny minimal spanning tree for born-digital images. In: Proceedings of the 10th IAPR International Workshop on Document Analysis Systems. DAS ’12, pp. 389–393 (2012)
Clavelli, A., Karatzas, D., Lladós, J.: A framework for the assessment of text extraction algorithms on complex colour images. In: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems. DAS ’10, ACM, pp. 19–26 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ghoshal, R., Banerjee, A. (2020). SVM and MLP Based Segmentation and Recognition of Text from Scene Images Through an Effective Binarization Scheme. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_20
Download citation
DOI: https://doi.org/10.1007/978-981-13-9042-5_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9041-8
Online ISBN: 978-981-13-9042-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)