Learn More
Writer identi"cation is carried out using handwritten text. The feature vector is derived by means of morphologically processing the horizontal pro"les (projection functions) of the words. The projections are derived and processed in segments in order to increase the discrimination e$ciency of the feature vector. Extensive study of the statistical(More)
In this work, a new approach for off-line signature recognition and verification is presented and described. A subset of the line, concave and convex family of curvature features is used to represent the signatures. Two major constraints are applied to the feature extraction algorithm in order to model the two step transitional probabilities of the(More)
— A novel offline signature modeling is introduced and evaluated which attempts to advance a grid based feature extraction method uniting it with the use of an ordered powerset. Specifically, this work represents the pixel distribution of the signature trace by modeling specific predetermined paths having Chebyshev distance of two, as being members of(More)
In this paper a feature vector, which has been used for curve coding, is evaluated in case of a signature verification scheme using a real time digital signal processor. The feature extraction method is based on morphologically processing the vertical projections of prescaled signature images. Coding of the curve profiles is carried out using morphological(More)
In this work the most important morphological granulometry, i.e. the pattern spectrum, is modeled, for the first time in the literature, as a first order Markov process. In addition, each of the terms of the process is shown to be normally distributed. The classification procedure followed for this specific application is based on modeling each separate(More)