This paper presents an HMM (Hidden Markov Model)-MLP (Multi-Layer Perceptro
n) hybrid model for recognising cursive script words. We adopt an explicit
segmentation-based word level architecture to implement an HMM classifier.
An efficient state transition model and a parameter re-estimation scheme ar
e introduced to use non-scaled and non-normalised symbol vectors without ha
ving to label primitive vectors. This approach brings well-formed discrete
signals for the variable state duration of the HMM. We also introduce a new
probability measure as well as conventional schemes to combine the propose
d HMMs and a general MLP. The main contributions of this model are a novel
design of the segmentation-based variable length HMMs, and an efficient met
hod of combining two distinct classifiers. Experiments have been conducted
using the legal word database of CENPARMI with encouraging results.