# Machine Learning Models for Artist Classification of Cultural Heritage Sketches
## 1 Introduction  
Art evaluation is a significant issue for art history and collectors. In the opinion of art experts, two of the vital problems in art authentication are artist attribution and forgery detection, which are part of the evaluation instruments for artworks [1]. If a proper detection is (mainly) successfully based upon physical and chemical characterization, the new attributions request more than that. A complex stylistic study must accompany these traditional artistic characterizations. With the growth of machine learning (ML) and artificial intelligence (AI) applications, we approach our artist attribution problem with specific, properly tuned award-gaining models such as VGG and ResNET. This detection algorithm allows us to implement flexible (error-permissive) algorithms that can look beyond the variations in data, thus making them appropriate for less specific fields, such as art-related scientific fields. AI-driven approaches use machine learning algorithms and image analysis techniques to address diverse challenges in analyzing original art [2]. Combining machine learning and image analysis, AI methods offer innovative solutions for analyzing original art. Such an approach allows human experts to gain objective, impartial insight, thus making the analysis results more relevant. Traceability issues often result in missing and incomplete ownership history, make the authentication task more difficult for older artworks, and make forgeries harder to identify. The art world is susceptible to forgeries, making it crucial to identify fakes and protect the value of genuine pieces [3]. Due to the improvements in forgery techniques, art authentication has proved to be a key factor with much attention, mainly due to digital image processing, forensics,and even legal cases. While publicly available artwork databases often exhibit deficiencies, our model was trained on images of drawings from private and public collections that were previously characterized by verified authenticity and comprehensive ownership history. This approach allowed us to concentrate on a more curated dataset. ML/AI models require a large dataset for training, testing, and validation, and have to deal with a large variety of features to analyze for artist discrimination within the same art school. We focused on stylistic elements such as brushstrokes and compositional patterns to encode each artist's style, thus providing a way to discriminate among them. While ML/AI models are pure nonlinear statistical models, the resulting model provides us with an artist-style classification technique; thus, it can flag potential forgeries that deviate from the learned style, providing valuable input data and highlighting points of analysis for subjective or sometimes biased evaluators. The proposed method can be improved but is not presented as a panacea for all attribution topics. However, it is a filter that can potentiate the importance of physical-chemical determinations or guide in-depth research--for example, through multi- or hyper-spectral imaging techniques or non-intrusive spectroscopic techniques for critical areas/points. A few years ago, the scattering transform was proposed for artist authentication in art images, and the analysis of selected entities was carried out to reveal the importance of deep layers using scattered, transformed, and rare linear classifiers for the authentication of art [4]. This research continued with a systematic analysis of machine learning methods for software testing [5] and different algorithms [6], as well as methods of authentication of paintings, based on the fusion of multiscale spatial-spectral entities and convolutional neural networks [7]. While most of the works included polychromies (paintings) with great results [8], we focused on images with a less critical volume of data information density, such as sketches, using a traditional approach. In this study, we developed three models for recognizing the strokes of some painters using different machine learning algorithms.