An Efficient Scheme for Automatic Pill Recognition Using Neural Networks

Abstract

An efficient scheme, capable of extracting key pill features, for an automatic pill recognition is proposed. The devised system involves a number of processes which starts with the thresholding applied to the input query pill image for extraction of the shape feature vector and generation of mask images. The extracted shape feature vector is used for shape recognition through a trained neural network. Information regarding the color and size of the pill is obtained by using the mask images and shape information. For pill imprint extraction, a modified stroke width transform (MSWT) and two-step sampling is applied. The extracted pill query features are compared with the feature values of the created database for recognition of the pill and its purpose. The proposed method is evaluated on a dataset of 2500 images and achieves an accuracy of 98% which shows the supremacy of the proposed method in comparison to the other similar pill recognition systems.