In Character Recognition, the Feature extraction has encompassed a well-known role. Here, Feature Extraction centered on Chain code (CC) is implemented. CC encodes every stroke with a string of numbers, in which every number signifies a specific direction wherein the subsequent point on the stroke is present. CC centered feature safeguard information and permits reasonable data to decrease. Disparate CC can signify the same shape since the CC is reliant on starting point. So here, Starting Point and rotation invariant feature extraction technique using Normalized Differential Chain Code (NDCC) is proposed. A two-stage classifier is employed for classification. Here, the NDCC feature is utilized in the pre-classifier and pre-processed (x,y) coordinates are used in the post classifier. In both stages K-NN classifier is used. This feature is verified in HP-Lab data that is present in the UNIPEN format. Investigational outcomes proved that the proposed feature enhances recognition accuracy over the selected dataset.
Rotation invariant feature extraction from 3-D acceleration signals
