ICTACT Journal on Image and Video Processing

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Nandinagari Palm Leaf Word Image Retrieval System


Affiliations
1 Department of Computer Science Engineering, Dayananda Sagar University, India
2 Department of Information Technology, University of Mysore, India
     

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This paper provides the first attempt for the recognition of Nandinagari handwritten word in a handwritten Palm Leaf manuscript. We take a set of very aged Palm Leaf and take a representative sample containing over 1000 characters and perform a set of preprocessing steps including background subtraction, de noising using Gaussian filter, contrast enhancement using histogram equalization and binarization using adaptive thresholding technique to obtain high quality readable manuscript. The words are subsequently extracted using annotation method to get a set of 100 meaningful Handwritten Nandinagari words of different sizes. A dictionary of these vocabulary words is formed and stored. Effective corner based feature extraction technique is applied to these images and the corresponding scale and rotation invariant features are extracted and stored in the database. The query word image is then compared with the dictionary words and the matched similar word images are retrieved.

Keywords

Invariant Feature Extraction, Scale Invariant Feature Transform, Background Subtraction, Denoising, Contrast Enhancement, Auto Thresholding.
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  • P. Visalakshi, “Nandinagari Script”, 1st Edition, DLA Publication, 2003.

  • Stanley Sternberg, “Biomedical Image Processing”, IEEE Computer, Vol. 16, No. 1, pp. 22-34, 1983

  • 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, 2014.

  • Rapeeporn Chamchong, Chun Che Fung and Kok Wai Wong, “Comparing Binarisation Techniques for the Processing of Ancient Manuscripts”, Proceedings of International Symposium on Entertainment Computing, pp. 55-64, 2010.

  • Salem Saleh Al Amri, N.V. Kalyankar and S. Khamitkar, “Deblured Gaussian Blurred Images”, Journal of Computing, Vol. 2, No. 4, pp. 132-139, 2010

  • Tarik Arici, Salih Dikbas and Yucel Altunbasak, “A Histogram Modification Framework and Its Application for Image Contrast Enhancement”, IEEE Transactions on Image Processing, Vol. 18, No. 9, pp. 1921-1935, 2009.

  • Mehmet Sezgin and Bulent Sankur, “Survey Over Image Thresholding Techniques and Quantitative Performance Evaluation”, Journal of Electronic Imaging, Vol. 13, No. 1, pp. 146-165, 2004.

  • J. Sauvola and M. Pietikainen, “Adaptive Document Image Binarization”, Pattern Recognition, Vol. 33, No. 2, pp. 225-236, 2000.

  • D.G. Lowe, “Distinctive Image Features from Scale-Invariant Key Points”, International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, 2004.

  • E.N. Mortensen, H. Deng and L. Shapiro, “A SIFT Descriptor with Global Context”, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 184-190, 2005.

  • Ives Rey-Otero, Jean-Michel Morel and Mauricio Delbarcio, “An Analysis of Scale-Space Sampling in SIFT”, Proceedings of IEEE International Conference on Image Processing, pp. 27-30, 2014.

  • Yishu Shi, Feng Xu, Feng-Xiang Ge Yishu Shi, Feng Xu and Feng-Xiang Ge, “SIFT-Type Descriptors for Sparse-Representation Based Classification”, Proceedings of 10th International Conference on Natural Computation, pp. 1-12, 2014.

  • D.G. Lowe, “Object Recognition from Local Scale-Invariant Features”, Proceedings of 7th IEEE International Conference on Computer Vision, pp. 20-27, 1999.

  • Martin A. Fischler and Robert C. Bolles, “Random Sample Consensus: A Paradigm For Model Fitting With Applications To Image Analysis And Automated Cartography”, Journal on Communications of the ACM, Vol. 24, No. 6, pp. 381-395, 1981.

  • Prathima Guruprasad and Jharna Majumdar, “Multimodal Recognition Framework: An Accurate and Powerful Nandinagari Handwritten Character Recognition Model”, Proceedings of International Multi Conference on Information Processing, pp. 836-844, 2016.

  • Prathima Guruprasad and Jharna Majumdar, “Achieving Premier Invariance to Scale and Rotation for Nandinagari Character Recognition by Comparing Multi Moment Features”, Proceedings of 6th International Conference on Advances in Computing and Communications, pp. 564-570, 2016.


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  • Nandinagari Palm Leaf Word Image Retrieval System

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Authors

Prathima Guruprasad
Department of Computer Science Engineering, Dayananda Sagar University, India
Guruprasad
Department of Information Technology, University of Mysore, India

Abstract


This paper provides the first attempt for the recognition of Nandinagari handwritten word in a handwritten Palm Leaf manuscript. We take a set of very aged Palm Leaf and take a representative sample containing over 1000 characters and perform a set of preprocessing steps including background subtraction, de noising using Gaussian filter, contrast enhancement using histogram equalization and binarization using adaptive thresholding technique to obtain high quality readable manuscript. The words are subsequently extracted using annotation method to get a set of 100 meaningful Handwritten Nandinagari words of different sizes. A dictionary of these vocabulary words is formed and stored. Effective corner based feature extraction technique is applied to these images and the corresponding scale and rotation invariant features are extracted and stored in the database. The query word image is then compared with the dictionary words and the matched similar word images are retrieved.

Keywords


Invariant Feature Extraction, Scale Invariant Feature Transform, Background Subtraction, Denoising, Contrast Enhancement, Auto Thresholding.

References