This article outlines the philosophy, design, and implementation of the Gradient, Structural, Concavity (GSC) recognition algorithm, which has been used successfully in several document reading applications. The GSC algorithm takes a quasi-multiresolution approach to feature generation; that is, several distinct feature types are applied at different scales in the image. These computed features measure the image characteristics at local, intermediate, and large scales. The local-scale features measure edge curvature in a neighborhood of a pixel, the intermediate features measure short stroke types which span several pixels, and the large features measure certain concavities which can span across the image. This philosophy, when coupled with the k-nearest neighbor classification paradigm, results in a recognizer which has both high accuracy and reliable confidence behavior. The confidences computed by this algorithm are generally high for valid class objects and low for nonclass objects. This allows it to be used in document reading algorithms which search for digit or character strings embedded in a field of objects. Applications of this paradigm to off-line digit string recognition and handwritten word recognition are discussed. Tests of the GSC classifier on large data bases of digits and characters are reported. 0 1996 John Wiley & Sons, Inc.A. Gradient Features. The gradient features are computed by convolving two 3 X 3 Sobel operators on the binary image. These operators approximate the x and y derivatives in the image at a pixel position (Fig. 2). The gradient of a center pixel is computed

No abstract

This article outlines the philosophy, design, and implementation of the Gradient, Structural, Concavity (GSC) recognition algorithm, which has been used successfully in several document reading applications. The GSC algorithm takes a quasi-multiresolution approach to feature generation; that is, several distinct feature types are applied at different scales in the image. These computed features measure the image characteristics at local, intermediate, and large scales. The local-scale features measure edge curvature in a neighborhood of a pixel, the intermediate features measure short stroke types which span several pixels, and the large features measure certain concavities which can span across the image. This philosophy, when coupled with the k-nearest neighbor classification paradigm, results in a recognizer which has both high accuracy and reliable confidence behavior. The confidences computed by this algorithm are generally high for valid class objects and low for nonclass objects. This allows it to be used in document reading algorithms which search for digit or character strings embedded in a field of objects. Applications of this paradigm to off-line digit string recognition and handwritten word recognition are discussed. Tests of the GSC classifier on large data bases of digits and characters are reported. 0 1996 John Wiley & Sons, Inc.

No abstract