Recognition of degraded characters using dynamic Bayesian networks
Pattern Recognition
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For off-line recognition of cursive handwritten word, the intersection between segmentation and recognition is complicated and makes the recognition problem still a challenging task. Hidden Markov Models (HMMs) have the ability to perform segmentation and recognition in a single step. In this paper we present an HMM based unsymmetric two-pass modeling approach for recognizing cursive handwritten word. The two-pass recognition approach exploits the segmentation ability of the Viterbi algorithm and creates three different HMM sets and carries out two passes of recognition. A weighted voting approach is used to combine results of the two recognition passes. High recognition rate has been achieved for recognizing cursive handwritten words with a lexicon of 1120 words. Experiment on NIST sample hand print data of ten different writers has also been carried out. The experimental results demonstrate that the two-pass approach can achieve better recognition performance and reduce the relative error rate significantly .