Describes a method for numeral character recognition. Initially, the image of an unknown numeral is pre-processed, and two feature sets are compiled and used for numeral character recognition. The first feature set is compounded by topological characteristics and by characteristics obtained from a pictorial distribution analysis of numeral images. The second feature set is the proper set of numeral images, after being normalized. The classification process is divided into two stages. In the first stage, the classification is based on the first feature set. In the second stage, Hopfield networks are used to find the most probable numeral class. Experimental results obtained from testing laboratory-prepared data and handwritten numerals extracted from real Brazilian bank checks show that recognition rates of 85% and 92.4% were achieved, respectively.
['Luan Ling Lee', 'Natanael Rodrigues Gomes']
Disconnected handwritten numeral image recognition