Numeral compared to the Hidden Markov Model

Numeral recognition is one among the most vital problems in pattern recognition. Its numerous applications like reading postal zip code, passport number, employee code, bank cheque processing and video gaming etc. To the best of our knowledge, little work has been done in Marathi language as compared with those for other Indian and non-Indian languages. This paper has discussed a novel technique for recognition of isolated Marathi numerals. It discusses a Marathi database and isolated numeral recognition system using Mel-Frequency Cepstral Coefficient (MFCC) and Distance Time Warping (DTW) as attributes. The precision of the pre-recorded samples is more than that of the real-time testing samples. We have also seen that the accuracy of the speaker dependent samples is higher than that of the speaker independent samples. Another method called HMM that statistically models the words is also presented. Experimentally, it is proved that recognition accuracy is higher for HMM compared with DTW, but the training procedure in DTW is very simple and fast, as compared to the Hidden Markov Model (HMM). The time needed for recognition of numerals using HMM is more as compared to DTW, as it has to go through the many states, iterations and many more mathematical modeling, so DTW is preferred for the real-time applications.


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