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. 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 weighing 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.
