Handwritten numeral recognition using gradient and curvature of gray scale image

Studies the use of curvature in addition to the gradient of gray-scale character images in order to improve the accuracy of handwritten numeral recognition. Three procedures, based on the curvature coefficient, biquadratic interpolation and gradient vector interpolation, are proposed for calculating the curvature of the equi-gray-scale curves of an input image. The efficiency of the feature vector is tested by recognition experiments for the handwritten numeral database IPTP CDROM1, which is a ZIP code database provided by the Institute for Posts and Telecommunications Policy (IPTP). The experimental results show the usefulness of the curvature feature, and a recognition rate of 99.40%, which is the highest that has ever been reported for this database, is achieved