Motivation: In India, the Language Kannada is an ancient and official language in Karnataka State. The study of ancient Kannada scripts from stone carvings, leaf, metal, cloth, paper and other sources enhances our knowledge on the traditions and culture practiced in Karnataka. Due to Poor Quality, variability and the contrast, the Kannada ancient scripts become very challenging to extract the information or to recognize the characters. Objectives: To design a suitable Optical Character Recognition (OCR) technique to read ancient Kannada scripts. Method: Clustering by fast search and find of density peaks is a state-of-the-art density-based clustering algorithm that can effectively find clusters with arbitrary shapes. However, it requires to calculate the distances between all the points in a data set to determine the density and separation of each point. Consequently, its computational cost is extremely high in the case of large-scale data sets. In this work the given document is preprocessed. The features alike SIFT and SURF are extracted and clustered using K-Means clustering. The similarity is computed using different measures. Findings: The classification accuracy was studied under different clustering methods like Kmeans, Agglomerative, Density based clustering with distance based measures like Euclidean and Manhattan. To evaluate the performance of the proposed method, we created our own database of Ashok, Kadamba, Hoysala and Mysuru scripts and experiment was conducted in a database of 4 classes under 70, 50 and 30 different training models from each class. Novelty: We propose a K-means clustering using SIFT and SURF for Kannada ancient manuscript.
Kannada is one of the famous ancient languages of the India and the official language of the State of Karnataka, which has a very large heritage. The digital analysis of these historical Kannada documents will provide us information about the culture and traditions that were practiced.
Retrieving such information from paper documents, palm leaves or from stone carvings will enhance our knowledge. Investigating Historical document isn’t straight advance procedure because of low quality, differentiation, contrast and covering of characters. In this analysis, the authors
propose a novel Scale invariant Feature Transform (SIFT) with deep learning classifier to recognize Historical kannada characters. To begin with, the character is divided utilizing Connected Component Analysis and later the Different SIFT Features are detached. At long last, form a powerful
convolutional neural system classifier to recognize the Historical kannada archives. Proposed tale schemes during the preprocessing stage to guarantee strong, precise and constant grouping. They assess their strategy all alone datasets their characterization results surpass 97% on all datasets,
which are superior to the cutting edge in this space.
Abstract:As the technology is advancing the communication medium is also changing. The wired medium of communication is getting transformed into wireless technology. One of the main wireless technologies used today is mobile ad-hoc networks (MANETS). One of the main issues is the security. Blackhole is one of the major attacks on the MANETS. This paper provides a survey how the blackhole attack is resisted using various protocols.
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