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Misclassification of medicine is perilous to the health of a patient, more soif the said patient is visually impaired or simply did not recognize the color,shape or type of medicine strip. This paper proposes a method foridentification of medicine strips by 2-D cepstral analysis of their imagesfollowed by performing classification that has been done using the K-NearestNeighbor (KNN), Support Vector Machine (SVM) and Logistic Regression (LR)Classifiers. The 2-D cepstral features extracted are extremely distinct to amedicine strip and consequently make identifying them exceptionally accurate.This paper also proposes the Color Gradient and Pill shape Feature (CGPF)extraction procedure and discusses the Binary Robust Invariant ScalableKeypoints (BRISK) algorithm as well. The mentioned algorithms were implementedand their identification results have been compared.

Medicine Strip Identification using 2-D Cepstral Feature Extraction and Multiclass Classification Methods