Ceramic tiles inspection placed an important role in various fields such as construction, architecture and so on. The quality of the ceramic tiles leads to improve the overall efficiency of the user defined application area. The manual inspection of the ceramic tiles fails to detect the quality of the tiles with effective manner. So, in this paper introduces the automatic ceramic tiles inspection process using the optimized data mining and image processing technology. The data mining techniques analyse the different tiles features using various optimization techniques and feature extraction methods that reduce the error rate while inspecting the ceramic tiles from the collection of tiles. In addition to this data mining technique, the image processing techniques examines the tiles images by utilizing the pre-processing, segmentation and feature extraction process. From the extracted features, tiles are inspected by applying the optimized data mining techniques such as artificial neural networks (ANN), probabilistic neural networks (PNN) and back propagation neural networks (BPNN). Thus the paper examines the comparative study of the ceramic tiles inspection process using the sensitivity, specificity and accuracy metrics.
