Ancient Indian Scripts Image Pre-Processing and Dimensionality Reduction for Feature Extraction and Classification: A Survey
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2015 by IJCTT Journal|
|Year of Publication : 2015|
|Authors : Abhishek Tomar, Minu Choudhary, Amit Yerpude|
|DOI : 10.14445/22312803/IJCTT-V21P1116|
Abhishek Tomar, Minu Choudhary, Amit Yerpude "Ancient Indian Scripts Image Pre-Processing and Dimensionality Reduction for Feature Extraction and Classification: A Survey". International Journal of Computer Trends and Technology (IJCTT) V21(2):85-93, March 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Engravings, inscriptions and epigraphs were used from archaic era to preserve knowledge and the great sayings. Diversified scripts are used in writing languages across the globe, in such an environment it is necessary to understand the script and the languages in the images or document prior to selecting an apt character detection and document analysis or prehistoric inscription analysis algorithm. Numerous methods for the automatic character identification and script recognition have been recommended so far. This manuscript is a terse survey on the image prior-processing techniques, segmentation techniques and feature extraction and classification via dimensionality reduction techniques. Broad research has previously been done in this domain but the ancient inscription character recognition is still challenging and needs more efficient techniques. This review will serve as basis for the preliminarily to image pre-processing and the efficacy of dimensionality reduction approaches in feature and classification.
 Nalin Warnajith, Atsushi Minato and Satoru Ozawa Dammi Bandra, "Creation of precise alphabets fonts of early Brahmi script from photographic data of Shri Lankan inscriptions," Canadian Journal on Artifical Intelligence, Machine Learning and Pattern Recognition, vol. Vol. 3, no. No. 3, pp. 33-39, 2012.
 Navya.K, Rajithkumar.B.K, Nagesh.C H.S. Mohana, "Interactive Segmentation for Character Extraction in Stone Inscriptions," in 2nd International Conference on Current Trends in Engineering and Technology, ICCTET’14, IEEE Conference Number - 33344, Coimbatore, India., July 8, 2014, pp. 321-327.
 Tulika Dube, and Adamane P. Shivaprasad Debashis Ghosh, "Script Recognition—A Review," IEEE transactions on pattern analysis and machine intelligence , vol. VOL. 32, no. NO. 12, pp. 2142-2161, DECEMBER 2010.
 brahmi.html. [Online]. http://www.ancientscripts.com/brahmi.html
 Bharath A. and Sriganesh Madhvanath, "OCR for Indic Scripts: Document Recognition and Retrieval," in Guide to OCR for Indic Scripts Advances in Pattern Recognition 2010. Hewlett-Packard Laboratories, Bangalore, India: Springer, 2010, pp. 209-234.  Line Eikvil, “Optical Character Recognition”. P.B. 114 Blindern, N- 0314Oslo: Norsk Regnesentral, December, 1993.
 B. Gatos, I. Pratikakis K. Ntirogiannis, "A combined approach for the 92 binarization of handwritten document images," Pattern Recognition Letters, vol. 35, pp. 3–15, 1 January 2014.
 C.R. and E.R. Woods Gonzalez, Digital Image Processing.: Prentice- Hall Inc., 2nd edition, 2002, pp. 75-278.
 Ventzas Dimitrios Ntogas Nikolaos, "A Binarization Algorithm For Historical Manuscripts ," in 12th WSEAS International Conference on COMMUNICATIONS, Heraklion, Greece, July 23-25, 2008.
 Suman Shrestha, "Image Denoising Using New Adaptive Based Median Filter," Signal & Image Processing : An International Journal (SIPIJ), vol. Vol.5 , no. No.4, pp. 1 -13, August 2014.
 K Srikanta Murthy, Arun Vikas Singh B Gangamma, "Restoration of Degraded Historical Document Image," Journal of Emerging Trends in Computing and Information Sciences, vol. VOL. 3, no. NO. 5, pp. 792- 798, May 2012.
 A. S. Chauhan and M. Dixit S. Silakari, "Image Segmentation Methods: A Survey Approach," in Fourth International Conference on Communication Systems and Network Technologies, 2014.
 Mehmet Sezgin and Bu¨lent Sankur, "Survey over image thresholding techniques and quantitative performance evaluation," Journal of Electronic Imaging , vol. 13, no. 1, pp. 146–165, January 2004.
 XIONG Fu-song, "Survey over image thresholding techniques based on entropy," in International Conference on Information Science, Electronics and Electrical Engineering (ISEEE), 2014, pp. 1330-1334.
 Sudipta Roy, O.Imocha Singh, Tejmani Sinam and Kh.Manglem Singh T.Romen Singh, "A New Local Adaptive Thresholding Technique in Binarization ," IJCSI International Journal of Computer Science Issues, vol. Vol. 8, no. 6, No 2, pp. 271-277, November 2011.  W. Niblack, An Introduction to Image Processing, Prentice-Hall, 1986, pp. pp:115-116.
 M. PietikaK inen J. Sauvola, "Adaptive document image binarization," Pattern Recognition, vol. 33 , pp. 225-236, 2000.
 Shijian Lu and Chew Lim Tan, "Binarization of Badly Illuminated Document Images through Shading Estimation and Compensation," in Ninth International Conference on Document Analysis and Recognition, ICDAR 2007, vol. 1, 2007, pp. 312 - 316.
 Shijian Lu, Chew Lim Tan Bolan Su, "A Robust Document Image Binarization Technique for Degraded Document Images," IEEE Transcation on Image Processing, vol. 22, no. 4, pp. 1057-7149, 2012.
 J Kumar and A G Ramakrishnan T Kasar, "Font and Background Color Independent Text Binarization," in In Proceedings of 2nd International Workshop on Camera Based Document Analysis and Recognition, 2007, pp. 3-9.
 Reza Farrahi Moghaddam and Mohamed Cheriet David Rivest- Hénault, "A local linear level set method for the binarization of degraded historical document images ," International Journal on Document Analysis and Recognition (IJDAR), vol. 15, no. 2, pp. 101- 124 , 2012.
 Lei Zhang, David Zhang and Hui Xu Quanxue Gao, "Independent components extraction from image matrix," Pattern Recognition Letters , vol. 31, pp. 171–178, 2010.
 Rishi Pandey, N. Jayanthi,Geetanjali Bhola and Santanu Chaudhury Indu Sreedevi, "NGFICA Based Digitization of Historic Inscription Images," ISRN Signal Processing, vol. vol. 2013, p. 7 pages, 2013.
 Ayush Tomar, Aman Raj and Santanu Chaudhury S. Indu. (2014, November) http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/ACCV _2014/pages/workshop13/index.html. [Online]. http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/ACCV _2014/pages/workshop13/pdffiles/w13-p3.pdf
 Lalit Prakash Saxena, "An effective binarization method for readability improvement of stain-affected (degraded) palm leaf and other types of manuscripts," CURRENT SCIENCE, vol. 107, no. 3, pp. 489-496, 10 AUGUST 2014.
 K. Fukunaga, Introduction to Statistical Pattern Recognition. San Diego, CA, USA,: Academic Press Professional, Inc., 1990.  R. Kharal, Semidefinite embedding for the dimensionality reduction of DNA microarray data, 2006.
 EO Postma, HJ Van den Herik LJP Van der Maaten, "Dimensionality reduction: A comparative review," Technical Report TiCC TR, 2009.
 N.P. Hughes and L. Tarassenko, "Novel signal shape descriptors through wavelet transforms and dimensionality reduction," In Wavelet Applications in Signal and Image Processing , vol. X, pp. 763–773, 2003.
 K. Chua, W. Chong, H. Lee, and Q. Gu L. Cao, "A comparison of pca,kpca and ica for dimensionality reduction in support vector machine," Neurocomputing, vol. 55, pp. 321-336, 2003.  S. Marsland, Machine Learning : An Algorithmic Perspective, CRC Press, 2009.
 Utpal, Atishay Jain, Anjan Maity, and Bhabatosh Chanda Garain, "Machine reading of camera-held low quality text images: an ICAbased image enhancement approach for improving OCR accuracy.," in In Pattern Recognition, 2008. ICPR 2008. 19th International Conference, 2008, pp. 1-4.
 G., Imran Khan, Naveen R. Chanukotimath, and Firoz Khan Keerthi Prasad, "On-line handwritten character recognition system for Kannada using Principal Component Analysis Approach: For handheld devices," in In Information and Communication Technologies (WICT) 2012 World Congress on, 2012, pp. 675-678.  Abdelmalek Zidouri, "PCA-based Arabic Character feature extraction," in In Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on, IEEE., 2007, pp. 1-4..
 A. V. N., and K. G. Hemantha. Manjunath, "Principal component analysis and generalized regression neural networks for efficient character recognition.," in In Emerging Trends in Engineering and Technology, 2008. ICETET`08. First International Conference on, IEEE, 2008., 2008, pp. 1170-1174..
 V., Sriganesh Madhvanath, and A. G. Ramakrishnan. Deepu, "Principal component analysis for online handwritten character recognition," in In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, IEEE, 2004., vol. 2, 2004, pp. 327-330.
 Purnima Kumari, Mondira Deori, Balbindar Kaur, Chandralekha Dey, and K. Das. Sharma, "Radon Transform and PCA based feature extraction to design an Assamese Character Recognition system," in Emerging Trends and Applications in Computer Science (NCETACS), 2012 3rd National Conference on, 2012, pp. 46 - 51.
 Delac Kresimir, Mislav Grgic, and Sonja Grgic. "A comparative study of PCA, ICA, and LDA.," in In Proc. of the 5th EURASIP Conference focused on Speech and Image Processing, Multimedia Communications and Services, 205, pp. 99-106.
 Delac Kresimir, Mislav Grgic, and Sonja Grgic, "Independent comparative study of PCA, ICA, and LDA on the FERET data set," in International Journal of Imaging Systems and Technology, vol. 15, 2005, pp. 252-260.
 Daohui, Xingang Zhao, Jianda Han, and Yiwen Zhao. Zhang, "A comparative study on PCA and LDA based EMG pattern recognition for anthropomorphic robotic hand," in In Robotics and Automation (ICRA), 2014 IEEE International Conference on, IEEE, 2014, 2014, pp. 4850-4855.
 Yunfei, and Ping Guo. Jiang, "Comparative studies of feature extraction methods with application to face recognition.," in In Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on,IEEE, 2007., 2007, pp. 3627-3632.
Optical Character recognition (OCR), Image Processing, Feature Extraction and Classification, Principal Component Analysis (PCA), Independent Component Analysis (ICA), Simultaneous Blind Source Extraction (SBSE).