Abstract

The segregation of image is most significant phase to identify borderline information of an image effectively. As various degradation exists in an image such as distorted pixel value, blurring in an image, poor luminance etc that affects the visual representation of an image. Using segmentation techniques, we attempts to improve the content of an image and make it clear for representation. There exist various edges and clustering based segmentation techniques such as perwitt, roberts, canny, sobel and K-means clustering that assist in segregating distortion information from a Modi character image to great extent. The comparative analysis of these segmentation techniques based on some performance parameters is performed to segment Modi character components. As a result, K- means clustering technique shows more appropriate outcome for segregating Modi numerals efficiently.

References

• Muhammad Waseem Khan, “A Survey: Image Segmentation Techniques”, International Journal of Future Computer and Communication, Vol. 3, No. 2, April 2014.
• C. Naveena and V.N. Manjunath Aradhya, “Handwritten Character Segmentation for Kannada Scripts”, IEEE World Congress on Information and Communication Technologies, November 2012.
• Jay H. Bosamiya, Palash Agrawal, Partha Pratim Roy and R. Balasubramanian “Script Independent Scene Text Segmentation using Fast Stroke Width Transform and GrabCut”, IEEE Asian Conference on Pattern Recognition, pp. 151-155, 2015.
• Naveen Tokas, Shruti Karkra and Manoj Kumar Pandey, “Comparison of Digital Image Segmentation Techniques- A Research Review”, International Journal of Computer Science and Mobile Computing, Vol. 5, Issue. 5, pp.215-220, May 2016.
• Manjula K. A, “Edge Detection as an Effective Technique in Image Segmentation for Image Analysis”, International Journal of Computer Science Trends and Technology, Vol. 2, Issue 4, pp. 126-131, December 2014.
• Sonam Saluja, Aradhana Kumari Singh, Sonu Agrawal, “A Study of Edge-Detection Methods”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, Issue 1, pp. 994-999, January 2013.
• Ali Abdo Mohammed Al-Kubati, Jamil A. M. Saif and Murad A. A. Taher, “Evaluation of Canny and Otsu Image Segmentation”, International Conference on Emerging Trends in Computer and Electronics Engineering, pp. 23-25, March 2012.
• Yuqin Yao, “Image Segmentation Based on Sobel Edge Detection”, International Conference on Advanced Materials and Computer Science, Vol.5, pp. 141-144, 2016.
• P. Sujatha and K. K. Sudha, “Performance Analysis of Different Edge Detection Techniques for Image Segmentation”, Indian Journal of Science and Technology, Vol. 8, No. 14, pp. 1-6, July 2015.
• Jaskirat Kaur, Sunil Agrawal and Renu Vig, “A Comparative Analysis of Thresholding and Edge Detection Segmentation Techniques”, International Journal of Computer Applications, Vol. 39, No. 15, pp. 29-34, February 2012.
• Dibya Jyoti Bora and Anil Kumar Gupta, “Clustering Approach towards Image Segmentation: An Analytical Study,” International Journal of Research in Computer Applications and Robotics Vol.2, Issue 7, pp. 115-124, July 2014.
• Preeti Panwar, Girdhar Gopal and Rakesh Kumar, “Image Segmentation using K-means clustering and Thresholding”, International Research Journal of Engineering and Technology, Vol. 3, Issue. 5, pp. 1787-1793, May 2016.
• Bhumika Solanki and Maya Ingle, “Performance Evaluation of Thresholding Techniques on Modi Script”, IEEE International Conference on Advanced Computation and Telecommunication, December 2018.
• Yusra A. Y. Al-Najjar and Der Chen Soong, “Comparison of Image Quality Assessment: PSNR, HVS, SSIM, UIQI”, International Journal of Scientific & Engineering Research, Vol. 3, Issue 8, pp. 1-5, August 2012.
• S. Rajkumar and G. Malathi, “A Comparative Analysis on Image Quality Assessment for Real Time Satellite Images”, Indian Journal of Science and Technology, Vol. 9, Issue 34, pp. 1-9, September 2016.