Researchers from Skoltech (part of the VEB.RF group) and Central University have developed an automated coin-grading approach that minimizes the subjectivity of manual assessments and overcomes the limitations of computer vision algorithms typically used for this purpose. The new approach provides more accurate and detailed grading across a wide variety of coin types. The approach was presented in the Journal on Computing and Cultural Heritage and further refined in two subsequent studies published in Expert Systems with Applications and IEEE Transactions on Instrumentation and Measurement.Researchers from Skoltech (part of the VEB.RF group) and Central University have developed an automated coin-grading approach that minimizes the subjectivity of manual assessments and overcomes the limitations of computer vision algorithms typically used for this purpose. The new approach provides more accurate and detailed grading across a wide variety of coin types. The approach was presented in the Journal on Computing and Cultural Heritage and further refined in two subsequent studies published in Expert Systems with Applications and IEEE Transactions on Instrumentation and Measurement.[#item_full_content]