We investigate a novel method for adaptively improving the machine recognition of handwritten words by applying a k-nearest neighbor (k-NN) classifier to the N-best word-hypothesis lists generated by a writer-independent hidden Markov model (HMM). Each new N-best list from the HMM is compared to the N-best lists in the k-NN classifier. A decision module is used to select between the output of the HMM and the matches found by the k-NN classifier. The N-best list chosen by the decision module can be automatically added to the k-NN classifier if it is not already in the k-NN classifier. This dynamic update of the k-NN classifier enables the system to adapt to new data without retraining. On a writer-independent set of 1158 handwritten words, this method reduces the error rate by approximately 30%. This method is fast and memory-efficient, and lends itself to many interesting generalizations.
['Thomas Kwok', 'Michael P. Perrone']
Adaptive N-best-list handwritten word recognition