Handwritten Character Recognition Using Structural Hidden Markov Models
نویسنده
چکیده
This paper introduces a methodology to recognize handwritten characters using “Structural Hidden Markov Models” (SHMM). The proposed approach is motivated by the need to model complex structures which are encountered in many areas such as speech/handwriting recognition, content-based information retrieval etc. The observations considered are strings that produce the structures. These observations are related in the sense they all contribute to produce a particular structure. The recognition efficiency of the system is 96.5%. The results reported in this component shows that the Structural Hidden Markov Model (SHMM) produces better recognition than the Hidden Markov Model.
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