Tanjina Piash Proma, Md Zakir Hossan, M Ashraful Amin
Proceedings of the 3rd International Conference on Graphics and Signal Processing
ACM, pp. 39–43
People of hazy or blurred vision or the elderly people finds it way too challenging just to identify the pills if they are out of the box or packet. And various pills of various shapes, size, texture, color comes with a diverse set of medicinal components. It creates confusion among pills of same color and shape to identify based on a specific texture. For visually impaired people, even if they configure the shape of the pill, the color information and the texts imprinted on the pill remains unknown to them. In this paper, the splitting processes of a dataset according to the number of colors and the texts imprinted on the pills, will be described. Initially the color information were extracted by segmenting pill region from pill image and then some statistical measurements i.e. Kurtosis and skewness, are calculated for probability distributions generated from the image histograms. Thus figuring out the how many colors the pill surface consists of. For the text recognition, the probable text region is detected for an error free text detection. For high quality image data, the reference images from NLM RxIMAGE database has been utilized. The overall accuracy of the proposed system for number of color determination is 95.6% and text recognition accuracy is 81.32%.
Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics