Text Detection in Natural Scene Images Using Morphological Component Analysis and Laplacian Dictionary

Shuping Liu; Yantuan Xian; Huafeng Li; Zhengtao Yu

doi:10.1109/JAS.2017.7510427

Volume 7 Issue 1

Jan. 2020

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2020 > 7(1): 214-222

Shuping Liu, Yantuan Xian, Huafeng Li and Zhengtao Yu, "Text Detection in Natural Scene Images Using Morphological Component Analysis and Laplacian Dictionary," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 214-222, Jan. 2020. doi: 10.1109/JAS.2017.7510427

Citation:

Shuping Liu, Yantuan Xian, Huafeng Li and Zhengtao Yu, "Text Detection in Natural Scene Images Using Morphological Component Analysis and Laplacian Dictionary," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 214-222, Jan. 2020. doi: 10.1109/JAS.2017.7510427

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Text Detection in Natural Scene Images Using Morphological Component Analysis and Laplacian Dictionary

doi: 10.1109/JAS.2017.7510427

School of Information Engineering and Automation and Key Laboratory of Intelligent Information Processing, Kunming University of Science and Technology, Kunming 650500, China

Funds:

the National Natural Science Foundation of China 61302041

the National Natural Science Foundation of China 61363044

the National Natural Science Foundation of China 61562053

the National Natural Science Foundation of China 61540042

the Applied Basic Research Foundation of Yunnan Provincial Science and Technology Department 2013FD011

the Applied Basic Research Foundation of Yunnan Provincial Science and Technology Department 2016FD039

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Abstract

Abstract

Text in natural scene images usually carries abundant semantic information. However, due to variations of text and complexity of background, detecting text in scene images becomes a critical and challenging task. In this paper, we present a novel method to detect text from scene images. Firstly, we decompose scene images into background and text components using morphological component analysis (MCA), which will reduce the adverse effects of complex backgrounds on the detection results. In order to improve the performance of image decomposition, two discriminative dictionaries of background and text are learned from the training samples. Moreover, Laplacian sparse regularization is introduced into our proposed dictionary learning method which improves discrimination of dictionary. Based on the text dictionary and the sparse-representation coefficients of text, we can construct the text component. After that, the text in the query image can be detected by applying certain heuristic rules. The results of experiments show the effectiveness of the proposed method.
- Dictionary learning,
- Laplacian sparse regularization,
- morphological component analysis (MCA),
- sparse representation,
- text detection.

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References(45)

References

[1]	B. A. Olshausen and D. J. Field, "Emergence of simple-cell receptive field properties by learning a sparse code for natural images, " Nature, vol. 381, no. 6583, pp. 607-609, Jun. 1996. https://www.ncbi.nlm.nih.gov/pubmed/8637596?ordinalpos=2&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DefaultReportPanel.Pubmed_RVDocSum
[2]	B. A. Olshausen and D. J. Field, "Sparse coding with an overcomplete basis set: a strategy employed by V1?" Vision Res., vol. 37, no. 23, pp. 3311-3325, Dec. 1997. https://www.ncbi.nlm.nih.gov/pubmed/9425546
[3]	S. H. Zhao and Z. P. Hu, "A modular weighted sparse representation based on fisher discriminant and sparse residual for face recognition with occlusion, " Inf. Process. Lett., vol. 115, no. 9, pp. 677-683, Sep. 2015. https://www.researchgate.net/publication/275219029_A_modular_weighted_sparse_representation_based_on_Fisher_discriminant_and_sparse_residual_for_face_recognition_with_occlusion
[4]	J. Q. Gu, H. F. Hu, and H. X. Li, "Local robust sparse representation for face recognition with single sample per person, " IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 547-554, Mar. 2018. http://www.cnki.com.cn/Article/CJFDTotal-ZDHB201802017.htm
[5]	M. Yang, L. Zhang, X. C. Feng, and D. Zhang, "Sparse representation based fisher discrimination dictionary learning for image classification, " Int. J. Comp. Vis., vol. 109, no. 3, pp. 209-232, Sep. 2014. doi: 10.1007/s11263-014-0722-8
[6]	J. C. Yang, K. Yu, Y. H. Gong, and T. Huang, "Linear spatial pyramid matching using sparse coding for image classification, " in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Miami, FL, USA, 2009, pp. 1794-1801.
[7]	M. Elad and M. Aharon, "Image denoising via sparse and redundant representations over learned dictionaries, " IEEE Trans. Image Process., vol. 15, no. 12, pp. 3736-3745, Dec. 2006. http://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM17153947
[8]	J. Mairal, M. Elad, and G. Sapiro, "Sparse representation for color image restoration, " IEEE Trans. Image Process., vol. 17, no. 1, pp. 53 -69, Jan. 2008. http://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM18229804
[9]	E. J. Cands, "Compressive sampling, " Marta Sanz Sol., vol. 25, no. 2, pp. 1433-1452, 2006. http://d.old.wanfangdata.com.cn/Periodical/dzkxxk201002040
[10]	L. L. Geng, Z. X. Ji, Y. H. Yuan, and Y. L. Yin, "Fractional-order sparse representation for image denoising, " IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 555-563, Mar. 2018. http://www.cnki.com.cn/Article/CJFDTotal-ZDHB201802018.htm
[11]	M. Zhao, S. T. Li, and J. Kwok, "Text detection in images using sparse representation with discriminative dictionaries, " Image Vis. Comp., vol. 28, no. 12, pp. 1590-1599, Dec. 2010.
[12]	W. M. Pan, T. D. Bui, and C. Y. Suen, "Text detection from scene images using sparse representation, " in Proc. 19th Int. Conf. Pattern Recognition, Tampa, FL, USA, 2008, pp. 1-5. https://www.researchgate.net/publication/224374857_Text_Detection_from_Scene_Images_Using_Sparse_Representation
[13]	T. H. Do, S. Tabbone, and O. Ramos-Terrades, "Text/graphic separation using a sparse representation with multi-learned dictionaries, " in Proc. 21st Int. Conf. Pattern Recognition (ICPR), Tsukuba, Japan, 2012, pp. 689-692. https://www.researchgate.net/publication/261202395_Textgraphic_separation_using_a_sparse_representation_with_multi-learned_dictionaries
[14]	Q. X. Ye and D. Doermann, "Text detection and recognition in imagery: A survey, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 7, pp. 1480-1500, Jul. 2015. https://www.researchgate.net/publication/273126576_Text_Detection_and_Recognition_in_Imagery_A_Survey
[15]	Z. Y. Liu and S. Sarkar, "Robust outdoor text detection using text intensity and shape features, " in Proc. 19th Int. Conf. Pattern Recognition, Tampa, FL, USA, 2008, pp. 1-4.
[16]	S. J. Lu and C. L. Tan, "Camera text recognition based on perspective invariants, " in Proc. 18th Int. Conf. Pattern Recognition, Hong Kong, China, 2006, pp. 1042-1045. https://www.researchgate.net/publication/220929286_Camera_Text_Recognition_based_on_Perspective_Invariants
[17]	R. J. Jiang, F. H. Qi, L. Xu, and G. R. Wu, "Using connected-components' features to detect and segment text, " J. Image Graph., vol. 11, no. 11, pp. 1653-1656, Nov. 2006. http://en.cnki.com.cn/Article_en/CJFDTotal-ZGTB200611034.htm
[18]	X. C. Yin, W. Y. Pei, J. Zhang, and H. W. Hao, "Multi-orientation scene text detection with adaptive clustering, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 9, pp. 1930-1937, Sep. 2015. https://www.ncbi.nlm.nih.gov/pubmed/26353137
[19]	H. W. Zhang, C. S. Liu, C. Yang, X. Q. Ding, and K. Q. Wang, "An improved scene text extraction method using conditional random field and optical character recognition, " in Proc. Int. Conf. Document Analysis and Recognition (ICDAR), Beijing, China, 2011, pp. 708-712. https://www.researchgate.net/publication/224265541_An_Improved_Scene_Text_Extraction_Method_Using_Conditional_Random_Field_and_Optical_Character_Recognition
[20]	K. Q. Wang and J. A. Kangas, "Character location in scene images from digital camera, " Pattern Recognit., vol. 36, no. 10, pp. 2287-2299, Oct. 2003. https://www.sciencedirect.com/science/article/abs/pii/S0031320303000827
[21]	H. Wang and J. Kangas, "Character-like region verification for extracting text in scene images, " in Proc. 6th Int. Conf. Document Analysis and Recognition, Seattle, WA, USA, 2001, pp. 957-962. https://www.researchgate.net/publication/220860146_Character-Like_Region_Verification_for_Extracting_Text_in_Scene_Images
[22]	K. I. Kim, K. Jung, and J. H. Kim, "Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 12, pp. 1631-1639, Dec. 2003.
[23]	J. Q. Yan, J. Li, and X. B. Gao, "Chinese text location under complex background using Gabor filter and SVM, " Neurocomputing, vol. 74, no. 17, pp. 2998-3008, Oct. 2011. https://www.sciencedirect.com/science/article/abs/pii/S0925231211003341
[24]	G. Zhou, Y. H. Liu, Q. Meng, and Y. L. Zhang, "Detecting multilingual text in natural scene, " in Proc. 1st Int. Symp. Access Spaces, Yokohama, Japan, 2011, pp. 116-120. Detecting multilingual text in natural scene
[25]	S. A. Angadi and M. M. Kodabagi, "A texture based methodology for text region extraction from low resolution natural scene images, " Int. J. Image Process., vol. 3, no. 5, pp. 229-245, 2009. http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_3554254c8d1e351c7ec5c6a06029f40a
[26]	R. R. Ji, P. F. Xu, H. X. Yao, Z. Zhang, X. S. Sun, and T. Q. Liu, "Directional correlation analysis of local Haar binary pattern for text detection, " in IEEE Int. Conf. Multimedia and Expo, Hannover, Germany, 2008, pp. 885-888. https://www.researchgate.net/publication/224327527_Directional_correlation_analysis_of_local_Haar_binary_pattern_for_text_detection
[27]	L. Sun, Q. Huo, W. Jia, and K. Chen, "A robust approach for text detection from natural scene images, " Pattern Recognit., vol. 48, no. 9, pp. 2906-2920, Sep. 2015. https://www.sciencedirect.com/science/article/abs/pii/S0031320315001260
[28]	Q. X. Ye, J. B. Jiao, J. Huang, and H. Yu, "Text detection and restoration in natural scene images, " J. Vis. Commun. Image Represent., vol. 18, no. 6, pp. 504-513, Dec. 2007. https://www.sciencedirect.com/science/article/pii/S104732030700065X
[29]	A. K. Jain and B. Yu, "Automatic text location in images and video frames, " Pattern Recognit., vol. 31, no. 12, pp. 2055-2076, Dec. 1998. https://www.sciencedirect.com/science/article/abs/pii/S0031320398000673
[30]	X. C. Yin, X. W. Yin, K. Z. Huang, and H. W. Hao, "Robust text detection in natural scene images, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 5, pp. 970-983, May 2014. https://www.ncbi.nlm.nih.gov/pubmed/24080709
[31]	Y. F. Pan, X. W. Hou, and C. L. Liu, "A hybrid approach to detect and localize texts in natural scene images, " IEEE Trans. Image Process., vol. 20, no. 3, pp. 800-813, Mar. 2011. http://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM20813645
[32]	H. Z. Chen, S. S. Tsai, G. Schroth, D. M. Chen, R. Grzeszczuk, and B. Girod, "Robust text detection in natural images with edge-enhanced maximally stable extremal regions, " in Proc. 18th Int. Conf. Image Processing, Brussels, Belgium, 2011, pp. 2609-2612. https://www.researchgate.net/publication/221120132_Robust_text_detection_in_natural_images_with_edge-enhanced_Maximally_Stable_Extremal_Regions
[33]	H. P. Le, N. D. Toan, S. Park, and G. Lee, "Text localization in natural scene images by mean-shift clustering and parallel edge feature, " in Proc. 5th Int. Conf. Ubiquitous Information Management and Communication, New York, NY, USA, 2011, pp. Article ID 116.
[34]	J. L. Starck, M. Elad, and D. Donoho, "Redundant multiscale transforms and their application for morphological component separation, " Adv. Imag. Electron Phys., vol. 132, pp. 287-348, 2004. doi: 10.1016/S1076-5670(04)32006-9
[35]	L. Rosasco, A. Verri, M. Santoro, S. Mosci, and S. Villa, Iterative Projection Methods For Structured Sparsity Regularization, Computer Science and Artifical Intelligence Laboratory Technical Report, MIT-CSAIL-TR-2009-OSO, Oct. 2009.
[36]	C. Yao, X. Bai, W. Y. Liu, Y. Ma, and Z. W. Tu, "Detecting texts of arbitrary orientations in natural images, " in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA, 2012, pp. 1083-1090. https://www.researchgate.net/publication/261348801_Detecting_Texts_of_Arbitrary_Orientations_in_Natural_Images
[37]	J. M. Bioucas-Dias and M. A. T. Figueiredo, "A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration, " IEEE Trans. Image Process., vol. 16, no. 12, pp. 2992-3004, Dec. 2007. doi: 10.1109/TIP.2007.909319
[38]	Y. Li, W. J. Jia, C. H. Shen, and A. van den Hengel, "Characterness: an indicator of text in the wild, " IEEE Trans. Image Process., vol. 23, no. 4, pp. 1666-1677, Apr. 2014. http://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM24808338
[39]	V. Khare, P. Shivakumara, and P. Raveendran, "A new histogram oriented moments descriptor for multi-oriented moving text detection in video, " Exp. Syst. Appl., vol. 42, no. 21, pp. 7627-7640, Nov. 2015. https://www.researchgate.net/publication/279154061_A_New_Histogram_Oriented_Moments_Descriptor_for_Multi-Oriented_Moving_Text_Detection_in_Video
[40]	Y. Zhang, J. H. Lai, and P. C. Yuen, "Text string detection for loosely constructed characters with arbitrary orientations, " Neurocomputing, vol. 168, pp. 970-978, Nov. 2015. doi: 10.1016/j.neucom.2015.05.028
[41]	D. Karatzas, F. Shafait, S. Uchida, M. Iwamura, L. G. I. Bigorda, S. R. Mestre, J. Mas, D. F. Mota, J. A. Almazan, and L. P. de las Heras, "Icdar 2013 robust reading competition, " in Proc. 12th Int. Conf. Document Analysis and Recognition, Washington, DC, USA, 2013, pp. 1484- 1493.
[42]	B. Epshtein, E. Ofek, and Y. Wexler, "Detecting text in natural scenes with stroke width transform, " in Proc. IEEE Conf. Computer Vision and Pattern Recognition, San Francisco, CA, USA, 2010, pp. 2963-2970. https://www.researchgate.net/publication/224164328_Detecting_Text_in_Natural_Scenes_with_Stroke_Width_Transform
[43]	R. M. Wang, N. Sang, and C. X. Gao, "Text detection approach based on confidence map and context information, " Neurocomputing, vol. 157, pp. 153-165, Jun. 2015. https://www.sciencedirect.com/science/article/abs/pii/S0925231215000442
[44]	G. Zhou, Y. H. Liu, and Z. Q. Tian, "Scene text detection with superpixels and hierarchical model, " in Proc. 19th Int. Conf. Image Processing, Orlando, FL, USA, 2012, pp. 1001-1004. https://www.researchgate.net/publication/261387291_Scene_text_detection_with_superpixels_and_hierarchical_model
[45]	X. R. Chen and A. L. Yuille, "Detecting and reading text in natural scenes, " in Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, Washington, DC, USA, vol. 2, pp. Ⅱ-366-Ⅱ-373, 2004. https://www.researchgate.net/publication/4082279_Detecting_and_reading_text_in_natural_scenes

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Highlights

Convert text detection into image decomposition to avoid influence of complex background of image.
SR-based pattern classification is introduced to increase dictionary discrimination capability.
A novel model is designed for separating the background and text, which detect texts accurately.

Text Detection in Natural Scene Images Using Morphological Component Analysis and Laplacian Dictionary

doi: 10.1109/JAS.2017.7510427

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通讯作者: 陈斌, bchen63@163.com

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