Abstract

Potsherds from Phra Mahathat Woramahawihan, a revered Buddhist temple in Thailand’s Nakhon Si Thammarat Province, were studied in the context of maritime Southeast Asia and local craftsmanship during the thirteenth–fourteenth century. The CIE L*a*b* and CIE L*C*h* colors of fine- and coarse-paste wares were analyzed using unsupervised and supervised machine learning algorithms. An illustrative example demonstrated the classification capability and cluster prediction. The example showed that supervised machine learning algorithms effectively discriminated between classes with similar characteristics, making colorimetry classification feasible. The kNN classifier achieved an overall accuracy of 87.10%, with high precision measurements (0.90). The supervised approach outperformed in color recognition and classification accuracy for the L*C*h* and combination of L*a*b* and L*C*h*. This strong inter-site discrimination capability is valuable in pottery research. The significantly lower color values of potsherds from Phra Mahathat Woramahawihan temple suggest multiple earthenware production sites in peninsular Thailand during the thirteenth–fourteenth century.