In this paper a handwritten character recognition algorithm based on structural characteristics, histograms and profiles, is presented. The well- known horizontal and vertical histograms are used, in combination with the newly introduced radial histogram, out-in radial and in-out radial profiles for representing 32 x 32 matrices of characters, as 280- dimension vectors.

The K-means algorithm is used for the classification of these vectors. Detailed experiments performed in NIST and GRUHD databases gave promising accuracy results that vary from 72.8% to 98.8% depending on the difficulty of the database and the character category.