Handwritten signature retrieval and identification

Many applications in image science require similarity retrieval of an image from a large collection of images. In such cases, image indexing becomes important for efficient organization and retrieval of images. This paper addresses this issue in the context of a database of handwritten signature images and describes a system for similarity retrieval and identification of handwritten signature images. The proposed system uses a set of geometric and topologic features to map a signature image into two strings of finite symbols. A local associative indexing scheme is then used on the strings to organize and search the signature image database. The advantage of the local associative indexing is that it is tolerant of missing features and allows queries even with partial signatures. The performance of the system has been tested with an image database of 120 signatures. The results obtained indicate that the proposed system is able to identify signatures with great accuracy even when a part of a signature is missing.