Abstract

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).

Highlights

  • This paper expands upon a previous publication [1] and is the natural continuation of an earlier study [2] which presented an industrial validator of expiration codes printed on aluminium or tin cans, called MONICOD

  • The previous study [2] described a computer vision solution, MONICOD, which is an industrial validator of expiration codes printed on aluminium and tin cans

  • The most important action that can be performed with a morphology is to compare it with another morphology and determine the distance or similarity between them; distance and similarity mean in this context the quantitative degree to which the two morphologies are different, or on the contrary, close [29]

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Summary

Introduction

This paper expands upon a previous publication [1] and is the natural continuation of an earlier study [2] which presented an industrial validator of expiration codes printed on aluminium or tin cans, called MONICOD. Speed is precisely the main reason why MONICOD is a validator and not a recogniser [7,8]. In other words, it is a matter of verification, not recognition. The existing literature for this kind of industrial validator has proven to be scarce [10,11,12,13]. Within this little explored field, with such singular demands, the authors present a solution with original proposals.

Character Segmentation Prior to Validation
Information Extracted in Previous Stages
Comparing Morphologies
Expected Code
Morphologies
Distance between Two Morphologies
The Morphological Family
The Morphological Family Database
Advantages and Disadvantages of Template Matching with MONICOD
Verification
Selection
The Selection Algorithm
Distance between Morphologies
Resolution
Learning
Learning During Learning Time
Automatic Subsystem
Manual Subsystem
Learning During Validation Time or “on-the-Fly” Learning
Description and General Conditions for Obtaining the Input Samples
Description of the Comparison with Other Classifiers
Results and Discussion
Conclusions and Future Work

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