GreekNet: Handwritten Greek Alphabet Recognition Using Explainable Parallel CNN with Attention Mechanisms

The Greek alphabet is one of the most ancient and influential writing systems, playing a crucial role in disciplines such as mathematics, physics, and engineering. Despite its significance, accurately recognizing handwritten Greek characters remains a challenge due to variations in individual handwriting and the absence of reliable techniques capable of handling these challenges. To overcome these challenges, we have first developed a dataset comprising 6,015 samples of lowercase Greek characters, collected from around 300 individuals to ensure a comprehensive representation of handwriting styles. Then we propose a novel machine learning (ML) architecture, GreekNet, that uses this dataset and combines a customized DenseNet121 with three parallel Custom Convolutional Neural Networks (CNN), each integrating different attention mechanisms: Channel Attention (CA), Squeeze-Excitation Attention (SEA), and Soft Attention (SA). These attention modules operate in parallel, with their outputs concatenated to generate more complex and distinguishable feature maps. Our proposed architecture achieved 99.35% accuracy. Using a Grad-CAM visualization approach we achieve explainability of our ML approach.