ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Machine Learning of Handwritten Nandinagari Characters using Vlad Vectors


Affiliations
1 Department of Computer Science and Engineering, Nitte Meenakshi Institute of Technology, India
     

   Subscribe/Renew Journal


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.
Subscription Login to verify subscription
User
Notifications
Font Size

  • 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.


Abstract Views: 378

PDF Views: 6




  • Machine Learning of Handwritten Nandinagari Characters using Vlad Vectors

Abstract Views: 378  |  PDF Views: 6

Authors

Prathima Guruprasad
Department of Computer Science and Engineering, Nitte Meenakshi Institute of Technology, India
Jharna Majumdar
Department of Computer Science and Engineering, Nitte Meenakshi Institute of Technology, India

Abstract


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