Journal Article

Model based odia numeral recognition using fuzzy aggregated features

Tusar Kanti MISHRA Banshidhar MAJHI Pankaj K SA Sandeep PANDA

2014

Overview

In 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 is 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 are 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.

• Publisher: Higher Education Press,Springer Nature B.V
• Subject: Computer Science / Handwriting recognition / handwritten character recognition / HMM / HMM模型 / Markov chains / OCR / Odia numeral / pattern recognition / Research Article / 下载 / 数字识别 / 模糊集合 / 特征向量 / 素材 / 网站 / 隐马尔可夫模型