Jagadeesh D. Pujari

Work place: S.D.M.College of Engineering & Technology Dharwar – 580 008, INDIA

E-mail: jaggudp@yahoo.com


Research Interests: Computer systems and computational processes, Pattern Recognition, Image Manipulation, Image Processing


Jagadeesh D.Pujari He has obtained Bachelor of Engineering in Computer Science in 1990 and MS in Software System (BITS PILANI) and Ph.D. in Computer Science in 2008. Currently he is working as Professor in the department of Information Science & Engineering, SDMCET, Dharwad, INDIA. His research areas of interests are Image Processing and Pattern Recognition.

Author Articles
Text Localization and Character Extraction in Natural Scene Images using Contourlet Transform and SVM Classifier

By Shivanand Seeri Jagadeesh D. Pujari P. S. Hiremath

DOI: https://doi.org/10.5815/ijigsp.2016.05.02, Pub. Date: 8 May 2016

The objective of this study is to propose a new method for text region localization and character extraction in natural scene images with complex background. In this paper, a hybrid methodology is suggested which extracts multilingual text from natural scene image with cluttered backgrounds. The proposed approach involves four steps. First, potential text regions in an image are extracted based on edge features using Contourlet transform. In the second step, potential text regions are tested for text content or non-text using GLCM features and SVM classifier. In the third step, detection of multiple lines in localized text regions is done and line segmentation is performed using horizontal profiles. In the last step, each character of the segmented line is extracted using vertical profiles. The experimentation has been done using images drawn from own dataset and ICDAR dataset. The performance is measured in terms of the precision and recall. The results demonstrate the effectiveness of the proposed method, which can be used as an efficient method for text recognition in natural scene images.

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Automatic Fungal Disease Detection based on Wavelet Feature Extraction and PCA Analysis in Commercial Crops

By Jagadeesh D. Pujari Rajesh.Yakkundimath Abdulmunaf. Syedhusain. Byadgi

DOI: https://doi.org/10.5815/ijigsp.2014.01.04, Pub. Date: 8 Nov. 2013

This paper describes automatic detection and classification of visual symptoms affected by fungal disease. Algorithms are developed to acquire and process color images of fungal disease affected on commercial crops like chili, cotton and sugarcane. The developed algorithms are used to preprocess, segment, extract and reduce features from fungal affected parts of a crop.  The feature extraction is done with discrete wavelet transform (DWT) and features are further reduced by using Principal component analysis (PCA). Reduced features are then used as inputs to classifiers and tests are performed to classify image samples. We have used statistical based Mahalanobis distance and Probabilistic neural network (PNN) classifiers. The average classification accuracies using Mahalanobis distance classifier are 83.17% and using PNN classifier are 86.48%

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