This book reviews the information provided by an artificial intelligence–based methodology for the characterization of artworks and the attribution of a piece to a particular artist. Until now, such approaches have been applied mainly to oil paintings, where supporting scientific evidence from underlying layers is available through IR and X‑ray imaging, as well as chemical analysis of pigments, binders, and resins.
The new approach explored in this book adapts these methods to ceramic decoration and the special pigments used for that purpose, which must withstand kiln‑firing temperatures of up to 1000 °C. As with oil paintings, the artistry of ceramic decorators has traditionally been assessed by art experts and connoisseurs, who have not previously had the advantage of considering associated scientific evidence and have therefore relied primarily on stylistic judgment.
This volume addresses that gap by focusing specifically on the work of the esteemed ceramic artist William Billingsley, who painted on porcelain from several manufactories, beginning with Derby and later including his own at Nantgarw, during the last two decades of the 18th century and the first two decades of the 19th century. Many examples of Billingsley’s artwork now reside in major museum collections worldwide.
The principle remains the same: the artificial intelligence procedure requires training a computer to recognize Billingsley’s verified works and then examine other pieces attributed to him to assess the correctness of their attribution, while also comparing his work to that of contemporaries such as Moses Webster, William Pegg, and Leonard Lead.
Table of Contents:
Artificial Intelligence and Machine Learning in the Attribution of Art Works.- Porcelain.- The Decoration of Porcelain.- Personae Surrounding William Billingsley and his Ceramic Art.- The Methodology and Science of Autoencoder based Artificial Intelligence Procedures.- The Art Works and Artificial Intelligence of some Contemporaries of William.- Case Studies and Artificial Intelligence Assisted Attributions in Porcelain.- Future Applications in Ceramic Art.
About the Author :
Howell Edwards is Professor Emeritus of Molecular Spectroscopy at the University of Bradford. He studied Chemistry at Jesus College, Oxford, completing his B.A. and B.Sc. before undertaking doctoral work in Raman spectroscopy. He later became a Research Fellow at Jesus College, Cambridge. After joining the University of Bradford as a Lecturer in Structural and Inorganic Chemistry, he became Head of the Department of Chemical and Forensic Sciences and was awarded a Personal Chair in Molecular Spectroscopy in 1996.
His career in spectroscopy has been recognised through major international awards, including the Sir Harold Thompson Award, the Charles Mann Award, the Emanuel Boricky Medal, and the Norman Sheppard Award. He has published more than 1,420 research papers on Raman spectroscopy and the analysis of materials in art, archaeology, and forensic science. He has also maintained a long-standing interest in the porcelains of William Billingsley, especially those made at Derby, Nantgarw, and Swansea.
Professor Edwards is the author of eleven books on ceramics, porcelain, and cultural heritage science, including Swansea and Nantgarw Porcelains: A Scientific Reappraisal; Nantgarw and Swansea Porcelains: An Analytical Perspective; Porcelain to Silica Bricks; 18th and 19th Century Porcelain Analysis; Porcelain Analysis and Its Role in the Forensic Attribution of Ceramic Specimens; Raman Spectroscopy in Cultural Heritage Preservation; Armorial Porcelain: The Genesis; Welsh Armorial Porcelains; The Pendock-Barry Porcelain Service; The Farnley Hall Service; and Blue by Fire. His forthcoming book, A-Marked Porcelains: Their History, Chemistry, Decoration and Attribution, co-authored with Ross and Gael Ramsay, is scheduled for 2026. He also serves as Honorary Scientific Adviser to the de Brecy Trust on the scientific evaluation of artworks.
Hassan Ugail is Professor of Visual Computing at the University of Bradford and Director of the Centre for Visual Computing and Intelligent Systems. With over three decades of experience in computer science, applied mathematics, and machine learning, his work centres on visual analysis, pattern recognition, and AI-enabled decision support.
He is internationally recognised for pioneering the use of artificial intelligence in the analysis and attribution of historical artworks. His research has shown how deep learning and handcrafted image features can detect subtle stylistic signatures in paintings and decorative art. He is especially known for his AI-assisted attribution studies on Raphael, including the widely reported analysis of the Madonna della Rosa, which helped establish computational connoisseurship as a credible method in art history.
This volume extends that work into ceramic art, applying similar methods to the decorated porcelain of William Billingsley and his contemporaries. By combining deep learning, one-class classification, and handcrafted image analysis, it introduces new tools for attribution and authentication in an area traditionally dominated by connoisseurship and physical examination.
Beyond cultural heritage, Professor Ugail has led significant research in biometrics, forensic imaging, healthcare AI, and public-sector decision support. He has worked with NHS Blood and Transplant and collaborated with teams at Oxford and Newcastle on NIHR-funded projects in medical image analysis and organ quality assessment. He has undertaken biometric research with Dubai Police and advised the United Nations on AI implementation. His work has generated patents, supported university spin-outs, and attracted funding from NIHR, EPSRC, UKRI, Innovate UK, DASA, DSTL, and the UK Ministry of Defence. His achievements have been recognised by the University of Bradford Vice-Chancellor's Award for Excellence in Knowledge Transfer (2010, 2019) and the Maldives National Award for Innovation (2011).
