Biomechanics-inspired analysis of the recognition function of recurrent neural networks in primary school math homework under low carbon background

In the traditional education assessment landscape, the manual grading of subjective exam questions poses significant challenges. The labor-intensive nature of this process and the potential for human error can negatively impact teaching and learning outcomes. As society transitions towards a low-carbon future, there is a pressing need to reform educational evaluation methods, reduce paper-based exams, and leverage advanced intelligent technologies. Inspired by the principles of biomechanics, this research introduces a novel image-based handwritten text recognition algorithm powered by recurrent neural networks, specifically designed for the automated scoring of primary school mathematics subjective questions. Drawing insights from the human visual and cognitive systems, the proposed approach mimics the hierarchical and adaptive nature of biological information processing to tackle the complexities inherent in handwritten text detection, recognition, and understanding. The study first constructs a comprehensive dataset of real primary school math exam answer sheets, capturing the diverse range of handwriting styles and mathematical notations. This dataset serves as a robust training and evaluation platform, akin to the diverse sensory inputs that biological systems process. The recurrent neural network architecture employed in this work exhibits biomimetic properties, such as the ability to dynamically process sequential information and adaptively refine its internal representations, much like the human brain's neural networks. This allows the algorithm to effectively handle the contextual cues and structural patterns present in handwritten mathematical responses, enabling accurate recognition and interpretation. Rigorous comparative and ablation experiments were conducted to assess the performance of the proposed algorithm. The results demonstrate high accuracy in recognizing and interpreting handwritten subjective responses, showcasing the practical value of this biomechanics-inspired approach. These findings align with the study's overarching goal of developing resource-saving and environmentally-friendly education evaluation systems, paving the way for the widespread adoption of intelligent technologies in the assessment of subjective questions. By drawing inspiration from the elegant and efficient information processing mechanisms observed in biological systems, this research contributes to the advancement of intelligent handwritten text recognition, ultimately supporting the transition towards a more sustainable and equitable educational landscape.