Abstract
Local features in Images can be described by the various algorithms in Computer Vision. Handwritten Devanagari word scripts are usually present in different illumination, sizes, orientation and occlusion. These are recognized by finding the points of Interest followed by the extraction of features around these interest points. In this paper we discuss extraction of Invariant Features from Handwritten Devanagari words of various forms using the Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) techniques. On analysis of the patterns of word images and their recognition capability, the criteria for robust detection is derived. A dataset of over 1000 Devanagari words of various sizes and forms is created. The features extracted from the query image is subjected to Image matching by comparison to those features in the database using Random sample consensus. Comparison of Results indicate that SURF is computationally efficient while SIFT is more apt for detecting deformed images.
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