Character Recognition with Hidden Markov Models (HMM)
Anthony Dipirro and Ji Mei - Undergraduate Symposium March 2012
Character recognition algorithms
generally fall inot one of two categories: neural network (NN) or
Hidden Markov Models HMM). We adopt the perspective from HMM's ,
namely, that character recognition is an example of a noisy channel
problem. The noisy channel problem assumes that a standard
form of information is being transmitted over a noisy channel and that
the noisy channel will distort the information received. The Viterbi
algorithm , employing probabilistic methods, determines the most likely
form of the original data.
Anthony and Ji used bit mapped images of characters and developed a
modified Viterbi algorithm for recognition. Their model achieved over
an 80% accuracy rate. To determine the generality of the algorithm, we
expanded the test-bed of characters to include Chinese characters and
again achieved the same level of accuracy.
Powerpoint slides from the presentation are here.