[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121103-en":3,"doc-seo-121103-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121103,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Findings on Machine Learning for Identification of Archaeological Ceramics - A Systematic Literature Review","Archaeological-ceramic identification supports cultural-heritage research by enabling accurate dating and classification of artifacts. Two major identification families exist: empirical expert-based methods and scientific analytical techniques, both of which can be time-consuming, costly, or limited by destructiveness, equipment needs, and database quality. A systematic literature review synthesizes 33 studies using machine learning, covering dataset-building, image processing, and classification algorithms. Results highlight deep learning’s effectiveness for automatic ceramic classification and emphasize the need for broader, standardized datasets to further improve robustness.","Findings on Machine Learning for Identification of Archaeological Ceramics: A Systematic Literature Review  \nZIYAO LING, GIOVANNI DELNEVO,(Member, IEEE), PAOLA SALOMONI,  \nAND SILVIA MIRRI,(Member, IEEE)  \nDepartment of Computer Science and Engineering, University of Bologna, 40127 Bologna, Italy Corresponding author: Giovanni Delnevo ([giovanni.delnevo2@unibo.it](giovanni.delnevo2@unibo.it))  \nABSTRACT The identification of archaeological ceramics is a relevant topic in the field of cultural heritage, and the history of archaeological ceramics can be traced back to prehistoric times. At present, there are two main methods for identifying archaeological ceramics, the empirical method and the technical one. In practice, these methods are costly or time-consuming. A systematic literature review of thirty-three studies on the identification of archaeological ceramics using machine learning is presented in this paper, includingthe collection process to build the dataset, the image processing of archaeological ceramic images, and the machine learning algorithms used for the classification. The main findings show the efficacy of deep learning  \nfor the automatic classification of archaeological ceramics compared to other approaches and highlight the need for more comprehensive and standardised datasets to further improve the automatic classification process.  \nINDEX TERMS Archaeological ceramic identification, archaeological ceramic classification, machine learning, deep learning, archaeological ceramics dataset.  \nI. INTRODUCTION  \nCeramics refer to a broad category of materials, typically inorganic and non-metallic, employed by humans for over 10,000 years [1] . It includes pottery, majolica, faience, terracotta, stone mass, and porcelain [2] . Their remarkable durability has allowed them to withstand the test of centuries, preserving a window into the past and safeguarding the stories of our ancestors [3] . Consequently, ceramics serve as chronological markers that are indispensable for reconstructing the past and comprehending the temporal progression of cultures, from their emergence to their decline, thereby enriching our understanding of the human journey through time [4] . Moreover, since they were among the earliest commodities traded between different regions and cultures, they serve as invaluable artefacts that unveil intricate trade networksand interactions between ancient societies [5] . They trace the paths of cultural exchange, uncover the complexities of  \nThe associate editor coordinating the review of this manuscript and  \napproving it for publication was Mostafa M. Fouda  .  \nancient trade routes, and highlight the interconnections of diverse civilizations across time and space [3] . For these reasons, it becomes of fundamental importance to accurately date and classify ancient ceramics.  \nCurrently, the methods for the identification of archaeological ceramics, mainly focused on pottery and porcelain, can be grouped into two main families [6] . On the one hand, thereis empirical identification [7] . It is a skilled process relying on the expertise of human analysts, that identify archaeological ceramics manually. Hence, it is a time-consuming activity, that often lacks objective constraints leading to inconsistent classifications. On the other hand, there are scientific identification methods that encompass techniques such as X-ray fluorescence analysis, thermoluminescence dating, and spectral analysis [8] . Thermoluminescence dating may cause damage to the porcelains themselves, resulting in irreversible damage [9] . While X-ray fluorescence analysis is a nondestructive technique [10], it does have some drawbacks for definitive identification: i) it primarily detects elements present in the porcelain and it misses subtle details, including  \nVOLUME 12, 2024  \n􀀊 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.  \nFor more information, see [https:","cbCaivsAm2Liytc6","https://ap.wps.com/l/cbCaivsAm2Liytc6","pdf",7578675,1,19,"English","en",105,"# Introduction\n## Background on ceramics and archaeological value\n## Two main identification approaches (empirical vs scientific)\n## Motivation for machine learning and computer vision\n# Systematic Literature Review (PRISMA)\n## Study selection and time window (2013–2023)\n## Collected evidence categories: datasets, image processing, algorithms","[{\"question\":\"What two main approaches are used to identify archaeological ceramics?\",\"answer\":\"The review distinguishes empirical identification based on expert manual analysis and scientific identification using analytical techniques such as X-ray fluorescence and thermoluminescence dating.\"},{\"question\":\"Why are empirical and scientific identification methods often impractical?\",\"answer\":\"Empirical methods are time-consuming and can produce inconsistent classifications due to limited objective constraints, while scientific methods may require expensive equipment and can involve limitations such as destructiveness or sensitivity to reference database quality.\"},{\"question\":\"What do the findings suggest about machine learning methods for ceramic identification?\",\"answer\":\"The synthesis of 33 studies reports that deep learning is effective for automatic classification compared with other approaches, while also indicating that improved performance depends on more comprehensive and standardized datasets.\"}]","Findings on Machine Learning for Identification of Archaeological Ceramics - A Systematic Literature Review | PDF",1785733744,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"findings-on-machine-learning-for-identification-of-archaeological-ceramics-a-systematic-literature-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/findings-on-machine-learning-for-identification-of-archaeological-ceramics-a-systematic-literature-review/121103/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What two main approaches are used to identify archaeological ceramics?","Question",{"text":75,"@type":76},"The review distinguishes empirical identification based on expert manual analysis and scientific identification using analytical techniques such as X-ray fluorescence and thermoluminescence dating.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why are empirical and scientific identification methods often impractical?",{"text":80,"@type":76},"Empirical methods are time-consuming and can produce inconsistent classifications due to limited objective constraints, while scientific methods may require expensive equipment and can involve limitations such as destructiveness or sensitivity to reference database quality.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the findings suggest about machine learning methods for ceramic identification?",{"text":84,"@type":76},"The synthesis of 33 studies reports that deep learning is effective for automatic classification compared with other approaches, while also indicating that improved performance depends on more comprehensive and standardized datasets.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]