A multimodal deep learning-driven model for assessing drawing composition quality

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In recent years, digital art education, computerized evaluation systems, and aesthetic computing have prioritized drawing composition quality assessment. Traditional assessments have relied on subjective judgment or single-modal analysis, limiting scalability and uniformity. Existing methods use visual characteristics or rule-based models, which cannot incorporate multimodal inputs, including visual layout and verbal descriptions or rubrics, restricting score validity. This research proposes that multimodal Deep Learning-Driven Modal(MM-DLN) be used to create a reliable and intelligent automatic drawing composition assessment framework. Visual and textual data are integrated into a single model to increase scoring precision and interpretability. The technique uses MM-DLN, a Multimodal Transformer-Based Scoring Network. It uses a Vision Transformer (ViT) to extract spatial and compositional aspects from the drawing and a Large Language Model Meta AI (Version 3) (LLaMA3) to analyze textual input. A cross-modal attention mechanism fuses these modalities, and a regression-based scoring system generates the score. The model highlights attention regions, revealing its score logic. The proposed MM-DLN model reduced MAE by 23.9% and improved PCC by 10.2% compared to the best-performing baseline, demonstrating enhanced prediction accuracy and more substantial alignment with expert-driven aesthetic evaluation. Performance was stable with noisy or stylistically diversified inputs. In conclusion, MM-DLN uses multimodal deep learning to assess drawing composition quality automatically in an accurate, scalable, and interpretable method.