Off-line writer verification based on simple graphemes

Abstract

A method to writer verification based on handwritten stroke analysis is presented. The proposed descriptors correspond to an estimation of the pressure applied when writing using the grayscale image of the stroke. These descriptors are obtained from individual and simple graphemes, in contrast with the complexity of the handwritten stroke used in the signature processing systems. In addition, a study is presented which suggests that the combination of descriptors of simple characters improves the recognition capacity of the method. The descriptors considered correspond to different accuracy degrees of pressure distribution representation. Specifically, from the simplest representation to a more complex one, the descriptors proposed are as follows: the width of the stroke, the gray level of the grapheme skeleton, the average of the gray levels on the perpendicular line to the skeleton, and the approximation transformation coefficients of the area of the grapheme. The advantage of these descriptors is that they are invariant to scale and rotation. The descriptors performance was assessed using the original images and also reduced versions based on traditional methods such as Principal Component Analysis and Discrete Cosine Transform. For the evaluation, a one-vs-all scheme was considered which is consistent with the problem of identity verification. It was implemented with Support Vector Machine classifiers trained with K-Fold Cross Validation. The efficient search of SVM hyperparameters was performed with the heuristic optimization algorithm Simulated Annealing. The evaluation of individual simple characters gives a high average of hits and the combination of characters even improves the performance, getting closer to 100% of hits in identity verification. Qualitative and quantitative comparison with other methods and descriptors has been also carried out with satisfactory results.