Authors:
Kesana Mohana Lakshmi,Tummala Ranga Babu,DOI NO:
https://doi.org/10.26782/jmcms.2019.02.00015Keywords:
Telugu script,texture features,statistical properties,non-subsampled contourlet transform,statistical parameters,feature vector and hammingdistance metric,Abstract
The most challenging task is searching Telugu script from the database because of difficulty in differentiating the Characteristics of the Telugu word or scripts. In this, we introduced robust approach for Telugu script retrieval using transformation-based methodology. Non-subsampled contourlet transform (NSCT) is utilized for texture classification which will function based on Non-subsampled pyramid filter bank (NSPFB) and Non-subsampled directional filter bank (NSDFB). Spatial dependence matrix is utilized to extract the texture features. In addition, image statistics is computed to enhance the retrieval performance further. Finally, hamming similarity metric is calculated which calculates the distance between trained and test word templates, which an effective distance metric over conventional Euclidean distance. In order to test, missing segment, noisy, corrupted and occlusion effected words are used as an input and taken into consideration multi conjunct vowel consonant clustered word images for showing the robustness of presented algorithm. In the substantial simulation analysis gives the presented technique finds most similar word images from database although if it is under testing conditions. Our presented scheme has superior performance compared to the traditional approaches described in the literature with respect to mean Average Precision (mAP) and mean Average Recall (mAR).Refference:
I.Arthur L. da Cunha, Jianping Zhou,and Minh N. Do, “The Non-subsampled Contourlet Transform: Theory, Design and Applications”, IEEE Transaction on Image Processing, Vol. 15, No. 10, pp. 3089-3100, 2006.
II.B. Verma, M. Blumenstein, S. Kulkarni, “Recent achievements in off-line handwriting recognition systems”, School of Information Technology, Griffith University, Gold Coast Campus.
III.C. V. Jawahar and A. Kumar, “Content-level Annotation of Large Collection of Printed Document Images”, In: Proc. of International Conf. on Document Analysis and Recognition, Parana, Brazil, 2007.
IV.C. V. Jawahar, M. N. S. S. K. Pavan Kumar, S. S. Ravi Kiran, “A Bilingual OCR for Hindi-Telugu Documents and its Applications”, Centre for Visual Information Technology, International Institute of Information Technology, Hyderabad.
V.D. G. Lowe, “Distinctive Image Features from Scale-Invariant Key points,” International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, 2004.
VI.Danish Nadeem and Saleha Rizvi, “Character recognition using template matching”, Department of Computer Science, JamiaMilliaIslamia, New Delhi, 2015.
VII.E Candes and D. Donoho, “Curvelets –a surprisingly effective nonadaptive representation for objects with edges.” In: A. Cohen, C. Rabut and L. Schumaker, Editors,Curves and Surface Fitting: Saint-Malo 1999, Vanderbilt University Press, Nashville, pp. 105–120, 2000.
VIII.E. Kreyszig, Advanced Engineering Mathematics, J. Willey & Sons Inc. 2011.
IX.E. Kreyszig, Advanced Engineering Mathematics, J. Willey & Sons Inc. 2011.
X.I. Z. Yalniz and R. Manmatha, “An Efficient Framework for Searching Text in Noisy Document Images”, IAPS International Workshop on Document Analysis Systems, Gold Cost, QLD, Australia, pp. 48-52, 2012.
XI.J. van Gemert, C. J. Veenman, A. W. M. Smeulders, and J.-M. Geusebroek, “Visual Word Ambiguity”, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 32, No. 7, pp.1271-1283, 2010.
XII.J. van Gemert, J.-M. Geusebroek, C. J. Veenman, and A. W. M. Smeulders, “Kernel Codebooks for Scene Categorization”, In: Proc. Of European Conf. on Computer Vision, Berlin, Heidelberg, pp. 696-709, 2008.
XIII.Jangala. Sasi Kiran, N. Vijaya Kumar, N. SashiPrabha and M. Kavya, “A Literature Survey on Digital Image Processing technique in character recognition of Indian languages”, International Journal of Computer Science and Information Technologies, Vol. 6, No. 3, pp. 2065-2069, 2015.
XIV.Jatin M Patil and Ashok P. Mane, “Multi Font and Size Optical Character Recognition Using Template Matching”, International Journal of Emerging Technology and Advanced Engineering, Vol. 3, No. 1, pp. 504-506, 2013.
XV.K Mohana Lakshmi and T RangaBabu, “Searching for Telugu Script in Noisy Images using SURF Descriptors”, IEEE 6th International Conference on Advance Computing, pp: 480-483, 2016.
XVI.K. Takeda, K. Kise, and M. Iwamura, “Real-time document image retrieval for a 10 Million pages database with a memory efficient and stability improved LLAH”, International Conf. on Document Analysis and Recognition, Beijing, China, pp. 1054-1058, 2011.
XVII.K.Mohana Lakshmi, Dr.T.Ranga Babu, “A Novel Telugu Script Recognition and Retrieval Approach Based on Hash Coded Hamming , ICCCPE(Springer LNS), 978-981-13-0211-4, 2018.
XVIII.KesanaMohana Lakshmi and TummalaRangaBabu, “A New Hybrid Algorithm for Telugu Word Retrieval and Recognition”, International Journal of Intelligent Engineering and Systems, Vol. 11, No. 4, pp.117-127, 2018.
XIX.M N Do and M Vetterli, “The contourlet transform: an efficient directional multiresolution image representation”, IEEE Transactions on Image Processing, Vol. 14, No. 12, pp. 2091-2106, 2005.
XX.M. J. Shensa, “The discrete wavelet transform: Wedding the àtrous and Mallat algorithms,” IEEE Trans. Signal Process., Vol. 40, No. 10, pp. 2464–2482, 1992.
XXI.M. Wenying and D. Zuchun, “A Digital Character Recognition Algorithm Based on the Template Weighted Match Degree”, International Journal of Smart Home, Vol.7, No. 3, pp. 53-60, 2013.
XXII.Md. Mahbubar Rahman, M. A. H. Akhand, Shahidul Islam, Pintu Chandra Shill and M. M. Hafizur Rahman, “Bangla Handwritten Character Recognition using Convolutional Neural Network”, International Journal of Image, Graphics and Signal Processing, Vol. 7, No. 8, pp. 42-49, 2015.
XXIII.N. Sharma, S. Chanda, U. Pal and M. Blumenstein, “Word-wise Script Identification from Video Frames”, In: Proc. of International Conf.on Document Analysis and Recognition, Washington, DC, USA, pp.867-871, 2013.
XXIV.N. Shobha Rani Vasudev T and Pradeep C.H. “A Performance Efficient Technique for Recognition of Telugu Script Using Template Matching”, International Journal of Image, Graphics and Signal Processing, Vol. 8, No. 3, pp.15-23, 2016. XXV.N. Shobha Rani, T. Vasudev, “A Generic Line Elimination Methodology using Circular Masks for Printed and Handwritten Document Images”, Emerging research in computing, information, communication and applications ELSEVIER science and technology, Vol. 3, No. 1, pp. 589-594, 2014.
XXVI.N.sharma, U.Pal, and M. Blumenstein, “A Study on Word Level Multi-script Identification from Video Frames”, In: Proc. of International Joint Conf. on Neural Networks, Beijing, China, pp.1827-1833, 2014.
XXVII.Nagasudha D and Y MadhaviLatha, “Keyword Spotting using HMM in Printed Telugu Documents”, In: Proc. of International Conf. on Signal Processing, Communication, Power and Embedded Systems, Paralakhemundi, India, pp: 1997-2000, 2016.
XXVIII.Nikhil Rajiv Pai and Vijaykumar S. Kolkure, “Design and implementation of optical character recognition using template matching for multi fonts size”, International Journal of Research in Engineering and Technology, Vol. 4, No. 2, pp. 398-400, 2015.
XXIX.P. Shivakumara, N. Sharma, U. Pal, M. Blumenstein, and C. L. Tan, “Gradient-Angular-Features for Word-wise Video Script Identification”, In: Proc. of International Conf. on Pattern Recognition, Stockholm, Sweden, pp.3098-3103, 2014.
XXX.R. Shekhar and C. V. Jawahar, “Word Image Retrieval Using Bag of Visual Words”, IAPS International Workshop on Document Analysis Systems, Gold Cost, QLD, Australia, pp. 297-301, 2012.
XXXI.Ravi Shekhar and C V Jawahar, “Word Image Retrieval Using Bag-of-Visual Words”, In: Proc. of IAPR International Workshop on Document Analysis Systems, Gold Cost, QLD,
XXXII.Rinki Singh, Manideep Kaur, “OCR for Telugu Script Using Back-Propagation Based Classifier”, International Journal of Information Technology and Knowledge Management, Vol. 2, No. 2, pp. 639-643, 2010.
XXXIII.S. Lazebnik, C. Schmid, and J. Ponce, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories”, In: Proc. of IEEE Computer Society Conf. onComputer Vision and Pattern Recognition, New York, USA, 2006.
XXXIV.Soumendu Das and Sreeparna Banerjee, “An Algorithm for Japanese Character Recognition”, International Journal of Image, Graphics and Signal Processing, Vol. 7, No. 1, pp. 9-15, 2014.
XXXV.Suman V Patgar, Vasudev T, Murali S, “A system for detection of fabrication in photocopy document”, Journal of Computer Science & Information Technology, Vol. 5, No. 14, pp. 29–35, 2015.
XXXVI.T. M. Rath and R. Manmatha, “Word spotting for historical documents”, International Journal of Document Analysis and Research, Vol. 9, No. 2-4,pp.139-152, 2007.
Kesana Mohana Lakshmi, Tummala Ranga Babu View Download