Glenn L. Cash

Optical character recognition by the method of moments

An investigation of the use of two-dimensional moments as features for recognition has resulted in the development of a systematic method of character recognition. The method has been applied to six machine-printed fonts. Documents used to test the method contained 24 lines of alphanumeric characters. Before scanning a document to be processed, a training document having the same font must be scanned and stored in memory. Characters on the training document are isolated by contour tracing, and then the 2D moments of each character are computed and stored in a library of feature vectors. The document to be recognized is then scanned, and the 2D moments of its characters are compared with those in the library for classification. In this paper we present the selection of a set of moments that provide good discrimination between characters, the comparison of three classification schemes, the selection of a weighting vector that improves the classification performance, and a series of experiments to determine how the recognition rate is affected by the number of library feature vector sets. Recognition rates between 98.5% and 99.7% have been achieved for all fonts tested.