© Research India Publications. To analyze coin image has to be segmented into two regions once of the coin and the area belonging to the background. We focus on the segmentation task as a preprocessing step for any automated text localization and feature extraction system. Firstly, we present a simple and flexible method for coin segmentation, based on double seed of region growing of coin on Gaussian distributions that allow segmenting various style of coin such as holed coins, triangle coins. Secondly, in the second stage, an active model based segmentation approach extracts precisely the coin from the image with features extraction. Thus, the coin is identified to a monetary class represented by a template coin. The similarity score of two coins is computed from feature constructed by feature point’s results with an identification accuracy of 94.4% on 2238 coin images of 120 classes.