This paper describes an OCR system for printed text documents in Kannada, a South Indian language. The input to the system would be the scanned image of a page of text and the output is a machine editable file compatible with most typesetting software. The system first extracts words from the document image and then segments the words into sub-character level pieces. The segmentation algorithm is motivated by the structure of the script. We propose a novel set of features for the recognition problem which are computationally simple to extract. The final recognition is achieved by employing a number of 2-class classifiers based on the Support Vector Machine (SVM) method. The recognition is independent of the font and size of the printed text and the system is seen to deliver reasonable performance.

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Many modern database applications require content-based similarity search capability in numeric attribute space. Further, users' notion of similarity varies between search sessions. Therefore online techniques for adaptively refining the similarity metric based on relevance feedback from the user are necessary. Existing methods use retrieved items marked relevant by the user to refine the similarity metric, without taking into account the information about non-relevant (or unsatisfactory) items. Consequently items in database close to non-relevant ones continue to be retrieved in further iterations. In this paper a robust technique is proposed to incorporate non-relevant information to efficiently discover the feasible search region. A decision surface is determined to split the attribute space into relevant and non-relevant regions. The decision surface is composed of hyperplanes, each of which is normal to the minimum distance vector from a nonrelevant point to the convex hull of the relevant points. A similarity metric, estimated using the relevant objects is used to rank and retrieve database objects in the relevant region. Experiments on simulated and benchmark datasets demonstrate robustness and superior performance of the proposed technique over existing adaptive similarity search techniques.

Inherent subjectivity in user's perception of an image has motivated the use of relevance feedback (RF) in the image retrieval process. RF techniques interactively determine the user's desired output or query concept, given the user's relevance judgments on a set of images. In this paper we propose a robust technique that utilizes non-relevant images to efficiently discover the relevant search region. A similarity metric, estimated using the relevant images is then used to rank and retrieve database images in the relevant region. The partitioning of the feature space is achieved by using a piecewise linear decision surface that separates the relevant and non-relevant images. Each of the hyperplanes constituting the decision surface is normal to the minimum distance vector from a non-relevant point to the convex hull of relevant points. Experimental results demonstrate significant improvement in retrieval performance for the small feedback size scenario over two well established RF algorithms.