  In this paper, we present a methodology for off-line handwritten character
recognition. The proposed methodology relies on a new feature extraction
technique based on structural characteristics, histograms and profiles. As
novelty, we propose the extraction of new eight histograms and four profiles
from the $32\times 32$ matrices that represent the characters, creating
256-dimension feature vectors. These feature vectors are then employed in a
classification step that uses a $k$-means algorithm. We performed experiments
using the NIST database to evaluate our proposal. Namely, the recognition
system was trained using 1000 samples and 64 classes for each symbol and was
tested on 500 samples for each symbol. We obtain promising accuracy results
that vary from 81.74\% to 93.75\%, depending on the difficulty of the character
category, showing better accuracy results than other methods from the state of
the art also based on structural characteristics.
