A Novel Approach for Evaluating Spatial-Temporal Synergy in Hybrid CNN-RNN and Vision Transformer Architectures

Pattern recognition is key to enhancing automated decision systems, particularly for image data analysis. As visual data grows in volume and complexity, robust models for accurate pattern interpretation are essential. This study examines multi-class image recognition via advanced architectures including CNNs, hybrid CNN-RNNs, and Vision Transformers (ViT), with comparative experiments. Performance metrics include accuracy, precision, recall, and F-score. The hybrid CNN-RNN model excelled, achieving 92.24 test accuracy by fusing spatial and temporal features. Precision and recall exceeded 90, proving effectiveness across patterns. This work demonstrates how advanced methods boost classification accuracy, aiding smarter AI for real-world tasks.