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Guruprasad, Prathima
- Machine Learning of Handwritten Nandinagari Characters using Vlad Vectors
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Authors
Affiliations
1 Department of Computer Science and Engineering, Nitte Meenakshi Institute of Technology, IN
1 Department of Computer Science and Engineering, Nitte Meenakshi Institute of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 8, No 2 (2017), Pagination: 1633-1638Abstract
This paper provides an early attempt to train and retrieve handwritten Nandinagari characters using one of the latest techniques in visual feature detection. The data set consists of over 1600 handwritten Nandinagari characters of different fonts, size, rotation, translation and image formats. In the Learning phase, we subject them to an approach where their recognition is effective by first extracting their key interest points on the images which are invariant to Scale, rotation, translation, illumination and occlusion. The technique used for this phase is Scale Invariant Feature Transform (SIFT). These features are represented in quantized form as visual words in code book generation step. Then the Vector of Locally Aggregated Descriptors (VLAD) is used for encoding each of the Image descriptors in the database. In the recognition phase, for query image, SIFT features are extracted and represented as query vector .Then these features are compared against the visual vocabulary generated by code book to retrieve similar images from the database. The performance is analysed by computing mean average precision .This is a novel scalable approach for recognition of rare handwritten Nandinagari characters with about 98% search accuracy with a good efficiency and relatively low memory usage requirements.Keywords
Handwritten Nandinagari Characters, Invariant Features, Scale Invariant Feature Transform, Image Vectorization, Indexing and Retrieval.
References
- P. Visalakshi, “Nandinagari Script”, 1st Edition, DLA Publication, 2003.
- 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 International Conference in Computer Vision and Pattern Recognition, Vol. 1, 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. 15-19, 2014.
- Ravi Shekhar and C.V. Jawahar, “Word Image Retrieval using Bag of Visual Words”, Proceedings of 10th IAPR International Workshop on Document Analysis Systems, pp. 1-6, 2012.
- Akanksha Gaur and Sunita Yadav, “Handwritten Hindi Character Recognition using K means Clustering and SVM”, Proceedings of 4th International Symposium on Emerging Trends and Technologies in Libraries and Information Services, pp. 115-119, 2015.
- Herve Jegou, Matthijs Douze, Cordelia Schmid and Patrick Perez, “Aggregating Local Descriptors into a Compact Image Representation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, Vol. 34, No. 9, pp. 1704-1716, 2013.
- Jonathan Delhumeau, Philippe-Henri Gosselin, Herve Jegou and Patrick Perez. “Revisiting the VLAD Image Representation”, Available at: https://hal.inria.fr/hal-00840653v1/document, Accessed on 2013.
- Relja Arandjelovic and Andrew Zisserman, “All about VLAD”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1578-1585, 2013.
- David Picard and Philippe-Henri Gosselin, “Improving Image Similarity with Vectors of Locally Aggregated Tensors”, Proceedings of IEEE International Conference on Image Processing, pp. 669-672, 2011.
- Prathima Guruprasad and Jharna Majumdar, “Handwritten Nandinagari Image Retrieval System based on Machine Learning Approach using Bag of Visual Words”, International Journal of Current Engineering and Scientific Research, Vol. 4, No. 4, pp. 163-168, 2017.
- Nandinagari Palm Leaf Word Image Retrieval System
Abstract Views :184 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science Engineering, Dayananda Sagar University, IN
2 Department of Information Technology, University of Mysore, IN
1 Department of Computer Science Engineering, Dayananda Sagar University, IN
2 Department of Information Technology, University of Mysore, IN
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 2 (2019), Pagination: 2103-2108Abstract
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
- 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.
- An Accurate and Robust Handwritten Nandinagari Recognition System
Abstract Views :170 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Dayananda Sagar University, IN
2 Department of Information Technology, Mindtree Limited, Bengaluru, IN
1 Department of Computer Science and Engineering, Dayananda Sagar University, IN
2 Department of Information Technology, Mindtree Limited, Bengaluru, IN
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 3 (2020), Pagination: 2119-2124Abstract
This paper is one of the early attempts for the recognition of Nandinagari handwritten characters. Good literature for finding specific types of key interest points using single approach is available for manuscripts. However, careful analysis indicate that a combinatorial approach is needed to be used in a collaborative manner for achieving good accuracy. On a variant data set of over 1000 Handwritten Nandinagari characters having different size, rotation, translation and image format, we subject them to an approach at every stage where their recognition is effective. In the first stage, the key interest points on the images which are invariant to Scale, rotation, translation, illumination and occlusion are identified by choosing Scale Invariant Feature Transform method. These points are then used to compute dissimilarity value with respect to every other image. Subsequently we subject these to Hierarchical Agglomerative cluster analysis for classification without supervision. Finally, for a query image, the same steps are followed and cluster mapping is analyzed. The result shows over 99% recognition, thus achieving a robust and accurate manuscript character recognition system.Keywords
Invariant Features, Scale Invariant Feature Transform, Nandinagari Handwritten Character Recognition, Hierarchical Agglomerative Clustering, Dissimilarity Matrix.References
- P. Visalakshy, “Nandinagari Script”, Dravidian Linguistics Association Publication, 2003.
- D.G. Lowe, “Distinctive Image Features from Scale-Invariant Key Points”, International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, 2004.
- 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, Jharna Majumdar and Guruprasad, “Scale Invariant Feature Extraction Techniques for Handwritten Nandinagari Vowels”, Proceedings of 3rd International Conference on Computing, Communication and Sensor Network, pp. 186-190, 2014.
- Ravi Shekhar and C.V. Jawahar, “Word Image Retrieval using Bag of Visual Words”, Proceedings of International Workshop on Document Analysis Systems”, pp. 1-7, 2012.
- D.G. Lowe, “Object Recognition from Local Scale-Invariant Features”, Proceedings of 17th IEEE International Conference on Computer Vision, pp. 122-129, 1999.
- Prathima Guruprasad, “Robust Handwritten Devanagari Word Identification using Scale Invariant Transform”, Proceedings of International Conference on Communication Networks and Signal Processing Communication Networks and Signal Processing, pp. 1-6, 2015.
- Prathima Guruprasad, “Handwritten Devanagari Word Recognition using Robust Invariant Feature Transforms”, Proceedings of International Conference on Applied and Theoretical Computing and Communication Technology, pp. 327-330, 2015.
- Panagiotis Antonopoulos, Nikos Nikolaidis and Ioannis Pitas, “Hierarchical Face Clustering using Sift Image Features”, Proceedings of International Symposium on Computational Intelligence in Image and Signal Processing, pp. 1-8, 2007.
- K.P. Sankar, V. Ambati, L. Pratha and C.V. Jawahar, “Digitizing A Million Books: Challenges for Document Analysis”, Proceedings of International Workshop on Document Analysis Systems, pp. 425-436, 2006.
- T.M. Rath and R. Manmatha, “Word Spotting for Historical Documents”, Master Thesis, Department of Computer Science¸ University of Massachusetts Amherst, pp. 1-152, 2007.
- A. Balasubramanian, M. Meshesha and C.V. Jawahar, “Retrieval from Document Image Collections”, Proceedings of International Workshop on Document Analysis Systems, pp. 1-12, 2006.
- E.N. Mortensen, H. Deng and L. Shapiro, “A SIFT Descriptor with Global Context”, Proceedings of International 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 International Conference on Image Processing, pp. 27-30, 2014.
- Yishu Shi, Feng Xu, Feng-Xiang Ge Yishu Shi, Feng Xu and Feng Xiang, “SIFT-Type Descriptors for Sparse Representation Based Classification”, Proceedings of 10th IEEE International Conference on Natural Computation, pp. 225-234, 2014.
- Y. Ke and R. Sukthankar, “PCA-SIFT: A More Distinctive Representation for Local Image Descriptors”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 506-513, 2004.
- Fionn Murtagh and Pierre Legendre, “Ward's Hierarchical Agglomerative Clustering Method: which Algorithms Implement Ward’s Criterion?”, Journal of Classification, Vol. 31, pp. 274-295, 2014.
- Prathima Guruprasad and Guruprasad K.S. Rao, “A Survey of Nandinagari Manuscript Recognition System”, International Journal of Science and Technology, Vol. 1, No. 1, pp. 30-36, 2011.