U Ravi Babu

Work place: GIET Rajahmundry, A.P, India

E-mail: uppu.ravibabu@gmail.com


Research Interests: Computational Science and Engineering


U Ravi Babu obtained his MSc Information Systems (IS) from AKRG PG College, Andhra University in the year 2003 and M.Tech Degree from RVD University in the year 2005. He is a member of SRRF-GIET, Rajahmundry. He is pursuing his Ph.D from AN University-Guntur in Computer Science & Engineering under the guidance of Dr V. Vijaya Kumar. He has published research papers in various National, Inter National conferences, proceedings. He is working as an Assistant Professor in GIET, Rajahmundry from July 2003 to till date. He is a life member of ISCA

Author Articles
Handwritten Digit Recognition Using Structural, Statistical Features and K-nearest Neighbor Classifier

By U Ravi Babu Aneel Kumar Chintha Y Venkateswarlu

DOI: https://doi.org/10.5815/ijieeb.2014.01.07, Pub. Date: 8 Feb. 2014

This paper presents a new approach to off-line handwritten numeral recognition based on structural and statistical features. Five different types of skeleton features: (horizontal, vertical crossings, end, branch, and cross points), number of contours in the image, Width-to-Height ratio, and distribution features are used for the recognition of numerals. We create two vectors Sample Feature Vector (SFV) is a vector which contains Structural and Statistical features of MNIST sample data base of handwritten numerals and Test Feature Vector (TFV) is a vector which contains Structural and Statistical features of MNIST test database of handwritten numerals. The performance of digit recognition system depends mainly on what kind of features are being used. The objective of this paper is to provide efficient and reliable techniques for recognition of handwritten numerals. A Euclidian minimum distance criterion is used to find minimum distances and k-nearest neighbor classifier is used to classify the numerals. MNIST database is used for both training and testing the system. A total 5000 numeral images are tested, and the overall accuracy is found to be 98.42%.

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Texture Classification Based on Texton Features

By U Ravi Babu V.Vijayakumar B Sujatha

DOI: https://doi.org/10.5815/ijigsp.2012.08.05, Pub. Date: 8 Aug. 2012

Texture Analysis plays an important role in the interpretation, understanding and recognition of terrain, biomedical or microscopic images. To achieve high accuracy in classification the present paper proposes a new method on textons. Each texture analysis method depends upon how the selected texture features characterizes image. Whenever a new texture feature is derived it is tested whether it precisely classifies the textures. Here not only the texture features are important but also the way in which they are applied is also important and significant for a crucial, precise and accurate texture classification and analysis. The present paper proposes a new method on textons, for an efficient rotationally invariant texture classification. The proposed Texton Features (TF) evaluates the relationship between the values of neighboring pixels. The proposed classification algorithm evaluates the histogram based techniques on TF for a precise classification. The experimental results on various stone textures indicate the efficacy of the proposed method when compared to other methods.

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