Abstract
This paper presents a new approach for text detection using sparse representation over learned dictionaries. More specifically, the K-SVD algorithm is used for constructing two dictionaries, one for the background and one for the text. Then, text detection is done by comparing the error constructions of each patch of image over two dictionaries. Results on ICDAR dataset present that proposed method is competitive related to state-of-the-art methods.
This research is funded by the Vietnam National University, Hanoi (VNU) under project number QG.18.04.
K. C. Santosh—IEEE senior member
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References
Aerschot, W., Jansen, M., Bultheel, A.: Normal mesh based geometrical image compression. Image Vis. Comput. 27(4), 459–468 (2009)
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. Sig. Process. 54(11), 4311–4322 (2006)
Angadi, S., Kodabagi, M.: A texture based methodology for text region extraction from low resolution natural scene images. In: Advance Computing Conference, pp. 121–128 (2010)
Belaid, A., Santosh, K., D’Andecy, V.P.: Handwritten and printed text separation in real document. In: The Thirteenth International Conference on Machine Vision Applications (2013)
Bui, T., Pan, W., Suen, C.: Text detection from natural scene images using topographic maps and sparse representations. In: The IEEE International Conference on Image Processing (2009)
Chen, D., Jean-Marc, O., Herve, B.: Text detection and recognition in images and video frames. Pattern Recogn. 37(3), 595–608 (2004)
Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM J. Sci. Comput. 20(1), 33–61 (1998)
Chen, X., Yuille, A.: Detecting and reading text in natural scenes. In: Proceeding of CVPR (2004)
Daubechies, I., Devore, R., Fornasier, M., Gunturk, C.: Iteratively reweighted least squares minimization for sparse recovery. Commun. Pure Appl. Math. 63(1), 1–38 (2009)
Do, T.H., Tabbone, S., Terrades, O.R.: Text/graphic separation using a sparse representation with multi-learned dictionaries. In: The International Conference on Pattern Recognition, pp. 689–692 (2012)
Do, T.H., Tabbone, S., Terrades, O.R.: Document noise removal using sparse representations over learned dictionary. In: ACM Symposium on Document Engineering, pp. 161–168 (2013)
Donoho, D., Elad, M.: Optimally sparse representation in general (nonorthogonal) dictionaries via ell1 minimization. PNAS 100(5), 2197–2202 (2003)
Elad, M.: Sparse and Redundant Representation: From Theory to Applications in Signal and Images Processing. Springer, New York (2010). https://doi.org/10.1007/978-1-4419-7011-4
Engan, K., Skretting, K., Husoy, J.H.: Family of iterative LS-based dictionary learning algorithm, ILS-DLA, for sparse signal representation. Digit. Signal Process. 17(1), 32–49 (2007)
Epshtein, B., Ofek, E., Wexler, Y.: Detecting text in natural scenes with stroke width transform. In: Proceedings of the CVPR (2010)
Ezaki, N., Bulacu, M., Schomaker, L.: Text detection from natural scene images: towards a system for visually impaired persons. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 2, pp. 683–686 (2004)
Jain, A., Yu, B.: Automatic text location in images and video frames. Pattern Recogn. 31(12), 2055–2076 (1998)
Jiang, R., Qi, F., Xu, L., Wu, G.: Using connected components’ features to detect and segment text. J. Image Graph. 11, 1653–1656 (2006)
Kim, K., Jung, K., Kim, J.: Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1631–1639 (2003)
Kumar, S., Gupta, R., Khanna, N., Chaudhury, S., Joshi, S.: Text extraction and document image segmentation using matched wavelets and MFR model. IEEE Trans. Image Process. 16(8), 2117–2128 (2007)
Lee, T.W., Lewicki, M.: Unsupervised image classification, segmentation and enhancement using ICA mixture models. IEEE Trans. Image Process. 11(3), 270–279 (2002)
Lienhart, R., Wernicke, A.: Localizing and segmenting text in images and videos. IEEE Trans. Circuits Syst. Video Technol. 12, 256–268 (2002)
Lim, J., Park, J., Medioni, G.: Text segmentation in color images using tensor voting. Image Vis. Comput. 25(5), 671–685 (2007)
Liu, Z., Sarkar, S.: Robust outdoor text detection using text intensity and shape features. In: The 19th International Conference on Pattern Recognition, pp. 1–4 (2008)
Lucas, S.M.: ICDAR 2005 text locating competition results. In: Proceedings of the ICDAR (2005)
Mallat, S.: Geometrical grouplets. Appl. Comput. Harmonic Anal. 26(2), 161–180 (2009)
Mallat, S.G., Zhang, Z.: Matching pursuits with time-frequency dictionaries. Sig. Process. 41(12), 3397–3415 (1993)
Mallat, S., Zhong, S.: Characterization of signals from multiscale edges. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 710–732 (1992)
Marial, J., Bach, F., Ponce, J., Sapiro, G.: Online dictionary learning for sparse coding. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 689–696 (2009)
Pan, W., Bui, T., Suen, C.: Text detection from scene images using sparse representation. In: Proceedings of the 19th International Conference on Pattern Recognition (ICPR 2008), pp. 1–5 (2008)
Pan, Y., Liu, C., Hou, X.: Fast scene text localization by learning-based filtering and verification. In: The 17th IEEE International Conference on Image Processing, pp. 2269–2272 (2010)
Park, J., Chung, H., Seong, Y.: Scene text detection suitable for parallelizing on multi-core. In: IEEE International Conference on Image Processing, pp. 2425–2428 (2009)
Pati, Y., Rezaiifar, R., Krishnaprasad, P.: Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: Proceedings of the 27th Annual Asilomar Conference on Signals, Systems, and Computers, pp. 40–44 (1993)
Santosh, K.C.: g-DICE: graph mining-based document information content exploitation. IJDAR 18(4), 337–355 (2015)
Santosh, K.C.: Document Image Analysis. Current Trends and Challenges in Graphics Recognition. Springer, Singapore (2018). https://doi.org/10.1007/978-981-13-2339-3
Skretting, K., Engan, K.: Recursive least squares dictionary learning algorithm. Sig. Process. 58(4), 2121–2130 (2010)
Temlyakov, V.N.: Weak greedy algorithms. Adv. Comput. Math. 12(2–3), 213–227 (2000)
Hoang, T.V., Tabbone, S.: Text extraction from graphical document images using sparse representation. In: Proceedings of the 9th International Workshop on Document Analysis Systems (2010)
Wright, J., Ganesh, A., Yang, A., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)
Yao, C., Bai, X., Liu, W., Ma, Y., Tu, Z.: Detecting text of arbitrary orientations in natural images. In: Proceedings of CVPR (2012)
Ye, Q., Jiao, J., Huang, J., Yu, H.: Text detection and restoration in natural scene images. J. Vis. Commun. Image Represent. 18(6), 504–513 (2007)
Yi, C., Tian, Y.: Text string detection from natural scenes by structure-based partition and grouping. In: Image Processing (2011)
Yi, C., Tian, Y.: Text string detection from natural scenes by structure-based partition and grouping. IEEE Trans. Image Process. 20(9), 2594–2605 (2011)
Zhao, M., Li, S., Kwok, J.: Text dectection in images using sparse representation with discriminative dictioanries. Image Vis. Comput. 28, 1590–1599 (2010)
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This research is funded by the Vietnam National University, Hanoi (VNU) under project number QG.18.04.
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Do, TH., Nguyen, T.M.H., Santosh, K.C. (2019). Text Extraction Using Sparse Representation over Learning Dictionaries. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1037. Springer, Singapore. https://doi.org/10.1007/978-981-13-9187-3_1
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