Advances in Information Mining

1-4

Authors: Reddy P.V.N., Satya Prasad K

In this paper a retrieval study for natural image learning environment is proposed. The content of this paper is a result of projects called Image content-based retrieval on a natural image database. The objective of this project is to develop an image content-based search engine, which can perform identity check of a natural image. It is well known that conventional natural image databases can only be retrieved by text-based query. In this paper we use the shape, color, and other features extracted from a captured natural image to search the natural image database. The developed technique is able to perform scale, translation, and rotation invariant matching between natural images. Currently, the database contains several hundreds of natural images. In future, we shall enhance the capability of the search engine to deal with more than 30,000 natural image species, which is the total amount of natural image species along the coast.

11-13

Authors: Ajmire P.E., Warkhede S.E

The work in this paper is deals with the recognition of handwritten Marathi vowels. Marathi is an Indo-Aryan language spoken by about 71 million people mainly in the Indian state of Maharashtra and neighbouring states. Marathi is also spoken in Israel and Mauritius. Marathi is thought to be a descendent of Maharashtri, one of the Prakrit languages which developed from Sanskrit. This work is based on invariant moments for recognition of isolated Marathi Handwritten Characters and their divisions. The proposed technique is independent of size, slant, orientation, translation and other variations in handwritten characters. Handwritten Marathi Characters/Numerals are more complex for recognition than corresponding English characters due to many possible variations in order, number, direction and shape of the constituent strokes. The work treats isolated Characters as an image of 40X40 pixel size. Seven invariant central moments of the image and two additional feature sets are evaluated. 10 samples of each number from 20 different writers have been sampled and prepared a database has been made. Seven central invariant moments are evaluated for each character and its parts by dividing it by two different ways. In all, there are 14 features corresponding to each character. The Gaussian Distribution Function has been adopted for classification. The average success rate of some vowels is compatible.