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$ciency of the feature vector. Extensive study of the statistical properties of the feature space is provided. Both Bayesian classi"ers and neural networks are employed to test the e$ciency of the proposed feature. The achieved identi"cation success using a long word exceeds 95%.
In REF , a new feature vector was employed by means of morphologically processing the horizontal profiles of the words.
Morphological waveform coding for writer identification
Pattern Recognition
Computer Science