Recognition of container code characters through gray-level feature extraction and gradient-based classifier optimization

This paper describes the recognition of container codecharacters in the project Mocont-II, where containerimages are taken in largely varying light situations. Therecognition system has to deal with gray-level charactersshowing a wide variability of brightness and contrast,varying inclination, segmentation uncertainties, damagedcharacters and the presence of shadows. Different sets offeatures were extracted directly from gray-level images,and a minimum distance classifier with a weighted metricwas used for recognition. To achieve good recognitionperformances, the feature weights and the prototype setswere optimized by a new gradient-based learningalgorithm that maximizes a fuzzy recognition ratefunctional.