Modern computer vision algorithms allow researchers and art historians to search for artist-characteristic contour extraction from sketches, thus providing accurate input for artwork analysis, for possible assignments and classifications, and also for the identification of the specific stylistic features. We approach this challenging task with three machine learning algorithms and evaluate their performance on a small collection of images from five distinct artists. These algorithms aim to find the most appropriate artist for a sketch (or a contour of a sketch), with promising results that have a higher level of confidence (around 92%). Models start from common Faster R-CNN architectures, reinforcement learning, and vector extraction tools. The proposed tool provides a base for future improvements to create a tool that aids artwork evaluators.

This paper presents a deep learning-based system designed for generating, storing, and retrieving embeddings, specifically tailored for analyzing craquelure networks in paintings. Craquelure, the fine pattern of the craquelure network formed on a painting’s surface over time, is a unique “fingerprint” for artwork item authentication. The system utilizes a modified VGG19 backbone, which effectively balances computational efficiency with the ability to extract rich, multi-scale features from high-resolution grayscale images. By leveraging this architecture, the model captures global structural patterns and local texture information, which are essential for reliable analysis.