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.
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