Model Based Odia Numeral Recognition using Fuzzy Aggregated Features
Tusar Kanti MISHRA, Banshidhar MAJHI, Pankaj K SA, Sandeep PANDA, Department of Computer Science and Engineering
n this paper, an efficient scheme for recognition of handwritten Odia numerals using Hidden Markov Model (HMM)
has been proposed. Three different feature vectors for each of the numeral are generated through a polygonal
approximation of object contour.
Subsequently, aggregated feature vector for each numeral is derived from these
three primary feature vectors using a fuzzy inference system. The final feature vector is divided into three levels
and interpreted as three different states for HMM. Ten different three-state ergodic Hidden Markov Models (HMMs) are
thus constructed corresponding to ten numeral classes and parameters are calculated from these models. For the
recognition of a probe numeral, its log-likelihood against these models is computed to decide its class label.
The proposed scheme is implemented on a dataset of 2500 handwritten samples and a recognition accuracy of 96:3%
has been achieved. The scheme is compared with other competent schemes. More in DOI: 10.1007/s11704-014-3354-9.
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