Abstract :
Abstract - Hidden Markov Models (HMM) have long been
a popular choice for Western cursive handwriting
recognition following their success in speech recognition.
Even for the recognition of Oriental scripts such as Chinese,
Japanese and Korean, Hidden Markov Models are
increasingly being used to model substrokes of characters.
However, when it comes to Indic script recognition, the
published work employing HMMs is limited, and generally
focused on isolated character recognition. In this effort, a
data-driven HMM-based handwritten word recognition
system for Hindi, an Indic script, is proposed. Though
Devanagari is the script for Hindi, which is the official
language of India, its character and word recognition pose
great challenges due to large variety of symbols and their
proximity in appearance. The accuracies obtained ranged
from 30% to 60% with lexicon. These initial results are
promising and warrant further research in this direction.
The results are also encouraging to explore possibilities for
adopting the approach to other Indic scripts as well.
