Automated visual inspection of imprinted pharmaceutical tablets

This paper is on automated visual inspection of tablets that may, in contrast to manual tablet sorting, provide objective and reproducible tablet quality assurance. Visual inspection of the ever-increasing numbers of produced imprinted tablets, regulatory enforced for unambiguous identification of active ingredients and dosage strength of each tablet, is especially demanding. The problem becomes more tractable by incorporating some a priori knowledge of the imprint shape and/or appearance. For this purpose, we consider two alternative automated tablet defect detection methods. The geometrical method, incorporating geometrical a priori knowledge of the imprint shape, enables specific inspection of the imprinted and non-imprinted tablet surface, while the statistical method exploits statistical a priori knowledge of tablet surface appearance, derived from a training image database. The two methods were evaluated on a large tablet image database, consisting of 3445 images of four types of imprinted tablets, with and without typical production defects. A ‘gold standard’ for testing the performances of the two inspection methods was established by manually classifying the tablets into good and five defective classes. The results, obtained by ROC (receiver operating characteristics) analysis, indicate that the statistical method yields better defect detection sensitivity and specificity than the geometrical method. Both presented image analysis methods are quite general and promising tools for automated visual inspection of imprinted pharmaceutical tablets.