Archeologists have taught computers to sort ancient pottery fragments

Archeologists at Northern Arizona University (NAU) have taught computers to sort pottery fragments by design and style to assist in classification and reconstruction. A team at NAU's department of anthropology used a form of machine learning known as Convolutional Neural Networks (CNNs) to create a computerized method that emulates the thought processes of the human mind when it analyzes visual information to rapidly and consistently sort thousands of pottery designs into categories. CNNs are commonly used in computer image recognition processes like comparing X-rays to medical conditions, matching images in search engines and in self-driving cars. "Now, using digital photographs of pottery, computers can accomplish what used to involve hundreds of hours of tedious, painstaking and eye-straining work by archaeologists who physically sorted pieces of broken pottery into groups, in a fraction of the time and with greater consistency," said study author Leszek Pawlowicz, in a release. The research results are due to be published in the June edition of the Journal of Archeological Science.