Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

A Novel Text Detection Technique Based On Corner Response

Author : Nishant Singh 1 Charul Bhatnagar 2

Date of Publication :22nd February 2018

Abstract: Data about the text incorporated in pictures and videos have a cardinal role in semantic assessments. In this paper, Novel text Detection and Localization (NTDL) algorithm is presented for text detection and localization in the background that incorporates noise in it. This algorithm is constituted by corner response. In contrast to the portions that do not contain text, there are some edges that are dense and corners in portions having text. So, some related strong reactions from regions of text and minimal reactions from portions that do not have text. These reactions furnish some cues that are highly useful for text detection and localization of pictures. By employing a basic schema constituted on the threshold, we obtain regions of candidates for text. These portions are evaluated by interlinking several characteristics like size and colour of linked devices. Lastly, the text line is identified exactly by the projection of response from corners. The outcomes from illustrations present exactness, speed and recalling for suggested methodology and we have obtained the recall of 93.25%, accuracy 97.96% and speed of 98.14% that greatly enhanced the performance of the system.

Reference :

    1. Anirban Bhar, Martin Haubrock, Anirban Mukhopadhya, Edgar Wingender, “Multiobjective triclustering of time-series transcriptome data reveals key genes of biological processes”, har et al. BMC Bioinformatics (2015)
    2. D. Gutiérrez-Avilés , C.Rubio-Escudero ,n, F.Martínez-Álvarez, J.C.Riquelme, “TriGen: genetic algorithm to mine triclusters in temporal gene expression data” , & 2013 Elsevier .V. ll rights reserved
    3. [3] Daxin Jiangy Jian Peiyz Murali Ramanathany Chun Tangy idong Zhangy “Mining Coherent Gene Clusters from Gene Sample Time Microarray Data” This research is partly supported by NSF grants DBI-0234895, IIS0308001 and NIH grant 1 P20 GM067650-01A1.
    4. [4] Duygu Dede, Hasan Ogul, “ three-way clustering approach to Cross-Species gene Regulation nalysis”, IEEE 2003.
    5. [5] H. A. Ahmed, P. Mahanta, D. K. Bhattacharyya, J. K. Kalita, . Ghosh “Intersected Coexpressed Subcube Miner: An effective triclustering algorithm” research project supported by DST, Govt. of India in collaboration with ISI, Kolkata.
    6. [6] Haoliang Jiang, Shuigeng Zhou, Jihong Guan, and Ying Zheng “gTRICLUSTER: More General and Effective 3D Clustering Algorithm for GeneSample-Time Microarray Data” J. Li et al. (Eds.): BioDM 2006, LNBI 3916, pp. 48–59, 2006 c Springer-Verlag Berlin Heidelberg 2006.
    7. K. Y. Yeung, C. Fraley, A. Murua, A. E. Raftery ,W. L. Ruzzo, “Model-based clustering and data transformations for gene expression data” , Oxford Journals 2001, Volume 17, Issue 10
    8. Lei Yu, Huan Liu, “Feature Selection for HighDimensional Data: A Fast Correlation-Based Filter Solution”, Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington DC, 2003
    9. Lizhuang Zhao, Mohammed J. Zaki, “TriCluster: An Effective Algorithm for Mining Coherent Clusters in 3D Microarray Data”, SIGMOD 2005 June 14-16, 2005, Baltimore, Maryland, USA.
    10. T. George ; S. Merugu , “ scalable collaborative filtering framework based on co-clustering, Data Mining”, Fifth IEEE International Conference
    11. T. R. Golub, D. K. Slonim, P. Tamayo, “Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring”, Science Vol 286, Issue 5439 15 October 1999.
    12. Teng L, Chan L: Discovering biclusters by iteratively sorting with weighted correlation coefficient in gene expression data. J Signal Process Syst 2008, 50(3):267-280.
    13. Xin Xu, Ying Lu, Anthony K. H. Tung, WeiWang, “Mining Shifting-and-Scaling CoRegulation Patterns on Gene”, Data Engineering, 2006. ICDE '06, Proceedings of the 22nd International Conference.

    1. R. C Gonzalez, (2009), Digital Image Processing, Pearson Education India.
    2. Jiawei Han, & Miche Line Kamber, (2006) Data Mining: Concepts and Techniques, 2nd ed., Elsevier.
    3. H. Li, D. Doermann, and O. Kia, “Automatic text detection and tracking in digital video,” IEEE Trans. Image Processing, vol. 9, no. 1, pp. 147– 156, 2000.
    4. B. Yu and A. Jain, “A generic system for form dropout,” IEEE Trans. Pattern Analysis And Machine Intelligence, vol. 18, pp. 1127–1134, 1996.
    5. A.K. Jain and B. Yu, “Automatic text location in images and video frames,” Pattern Recognition, vol. 31, no. 12, pp. 2055–2076, 1998. [6] M.R. Lyu and J.-Q. Song, “A comprehensive method for multilingual video text detection, localization, and extraction,” IEEE Trans. Circuits and System for Video Technology, vol. 15, no. 2, pp. 243–255, 2005
    6. Xiaojun Li,WeiqiangWang, Shuqiang Jiang, Qingming Huang, and Wen Gao, “Fast and effective text detection,” in Proc. of the IEEE International Conference on Image Processing (ICIP), 2008. iyan Hu and Minya Chen, “Adaptive fre/spl acute/chet kernel based support vector machine for text detection,” in Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2005.
    7. Xian sheng Hua, Xiang rong Chen, Liu Wenyin, and Hong jiang Zhang, “Automatic location of text in video frames,” in Proceeding of ACM Multimedia 2001 Workshops: Multimedia Information Retrieval (MIR2001), 2005.
    8. C.G. Harris and M.J. Stephens, “A combined corner and edge detector,” in Proceeding of the 4th Alvey Vision Conference, 1988, pp. 147–152.
    9. Rainer Lienhart and Axel Wernicke, “Localizing and segmenting text in images and videos,” IEEE Trans. Circuits and System for Video Technology, vol. 12, pp. 256–267, 2002.
    10. J Gllavata, R Ewerth and B Freisleben, ―Finding Text in Images via Local Thresholding‖, IEEE, vol 1, pp. 539-542,Dec 2003.
    11. Li Sun, Guizhong Liu, Xueming Qian, Danping Guo," A Novel Text Detection And Localization Method Based On Corner Response " ©2009 IEEE
    12. L.Sun, G.Liu, X.Qian and D.Guo, ―A Novel Text Detection and Localization Method based on Corner Response‖, IEEE, vol 2, pp. 390-393, Jun-Jul 2009.
    13. X.Zahu, K.Lin, Y.Fu, Y.Hu, Y.Liu and T.Huang, ―Text From Corners: A Novel Approach to Detect Text and Caption in Videos‖ IEEE, vol 20, pp. 790-799, Mar 2011.
    14. Xueming Qian, Guizhong Liu, Huan Wang, and Rui Su, “Text detection, localization, and tracking in compressed video,” Signal Processing: Image Communication, vol. 22, no. 9, pp. 752–768, 2007.
    15. Puri and S Kaushik, An Enhanced Fuzzy Similarity Based Concept Mining Model Using Feature Clustering‖, IEEE, pp. 1-6, March 2012
    16. S Puri and S Kaushik, A Technical Study and Analysis on Fuzzy Similarity Based Models for Text Classification‖, IJDKP, vol. 2, No. 2, pp. 1-15 Mar 2012.
    17. S. Puri and S. P. Singh, “Sentence Detection and Extraction in Machine Printed Imaged Document using Matching Technique,” RAECS, unpublished, 2015.

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