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This paper expands upon a previous publication and is the natural continuation of an earlier study which presented an industrial validator of expiration codes printed on aluminium or tin cans, called MONICOD. MONICOD is distinguished by its high operating speed, running at 200 frames per second and validating up to 35 cans per second. This paper adds further detail to this description by describing the final stage of the MONICOD industrial validator: the process of effectively validating the characters. In this process we compare the acquired shapes, segmented during the prior stages, with expected character shapes. To do this, we use a template matching scheme (here called “morphologies”) based on bitwise operations. Two learning algorithms for building the valid morphology databases are also presented. The results of the study presented here show that in the acquisition of 9885 frames containing 465 cans to be validated, there was only one false positive (0.21% of the total). Another notable feature is that it is at least 20% faster in validation time with error rates similar to those of classifiers such as support vector machines (SVM), radial base functions (RBF), multi-layer perceptron with backpropagation (MLP) and k-nearest neighbours (KNN).

Expiration date labels are ubiquitous in the food industry. With the passage of time, almost any food becomes unhealthy, even when well preserved. The expiration date is estimated based on the type and manufacture/packaging time of that particular food unit. This date is then printed on the container so it is available to the end user at the time of consumption. MONICOD (MONItoring of CODes); an industrial validator of expiration codes; allows the expiration code printed on a drink can to be read. This verification occurs immediately after printing. MONICOD faces difficulties due to the high printing rate (35 cans per second) and problematic lighting caused by the metallic surface on which the code is printed. This article describes a solution that allows MONICOD to extract shapes and presents quantitative results for the speed and quality. [ABSTRACT FROM AUTHOR]

Expiration date labels are ubiquitous in the food industry. With the passage of time, almost any food becomes unhealthy, even when well preserved. The expiration date is estimated based on the type and manufacture/packaging time of that particular food unit. This date is then printed on the container so it is available to the end user at the time of consumption. MONICOD (MONItoring of CODes); an industrial validator of expiration codes; allows the expiration code printed on a drink can to be read. This verification occurs immediately after printing. MONICOD faces difficulties due to the high printing rate (35 cans per second) and problematic lighting caused by the metallic surface on which the code is printed. This article describes a solution that allows MONICOD to extract shapes and presents quantitative results for the speed and quality.

This paper expands upon a previous publication and is the natural continuation of an earlier study which presented an industrial validator of expiration codes printed on aluminium or tin cans, called MONICOD. MONICOD is distinguished by its high operating speed, running at 200 frames per second and validating up to 35 cans per second. This paper adds further detail to this description by describing the final stage of the MONICOD industrial validator: the process of effectively validating the characters. In this process we compare the acquired shapes, segmented during the prior stages, with expected character shapes. To do this, we use a template matching scheme (here called "morphologies") based on bitwise operations. Two learning algorithms for building the valid morphology databases are also presented. The results of the study presented here show that in the acquisition of 9885 frames containing 465 cans to be validated, there was only one false positive (0.21% of the total). Another notable feature is that it is at least 20% faster in validation time with error rates similar to those of classifiers such as support vector machines (SVM), radial base functions (RBF), multi-layer perceptron with backpropagation (MLP) and k-nearest neighbours (KNN). [ABSTRACT FROM AUTHOR]