Studies on the Comparison between DP Matching and HMM for Aerial Handwritten Hiragana Character Recognition

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  • 空中手書き文字入力におけるDP マッチングとHMM の比較に関する研究
  • クウチュウ テガキキ モジ ニュウリョク ニ オケル DP マッチング ト HMM ノ ヒカク ニ カンスル ケンキュウ

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This paper presents a method to recognize a character handwritten in the air. We had prototyped an aerial handwritten hiragana character recognition system that detect single character period by hovering in the air using DP (dynamic programming) matching. In this time, we investigate an aerial handwritten character recognition system using Hidden Markov Model (HMM). HMM theory is an extension of the Markov Model process. It has found uses in such areas as speech recognition, target tracking and word recognition. We implemented HMM using Baum?Welch algorithm. The Baum?Welch algorithm uses the well known EM algorithm to find the maximum likelihood estimate of the parameters of a hidden Markov model given a set of observed feature vectors. We compared correct recognition rate of using DP matching and using HMM. We estimated features of both methods.

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