  This paper presents results of a study of the performance of several base
classifiers for recognition of handwritten characters of the modern Latin
alphabet. Base classification performance is further enhanced by utilizing
Viterbi error correction by determining the Viterbi sequence. Hidden Markov
Models (HMMs) models exploit relationships between letters within a word to
determine the most likely sequence of characters. Four base classifiers are
studied along with eight feature sets extracted from the handwritten dataset.
The best classification performance after correction was 89.8%, and the average
was 68.1%
