A system for intelligent verification of assignment files that integrates computer vision and natural language processing
An end-to-end assignment verification system that uses computer vision (CV) and natural language processing (NLP) to handle various complex types of modern academic documents. The solution uses multi-scale convolutional neural networks to capture writing and layout features. Using transformer-based structures for semantic parsing and segmentation. At its core is an adaptive cross-modal fusion mechanism, used to synchronize and merge visual and textual features to enhance the ability to recognize anomalous structures, fraudulent handwriting, and semantic mismatches. To train and test the system, we used a large, meticulously annotated dataset containing over 15,000 assignment papers, including handwritten, typed, and mixed-type papers. Many experiments compared the proposed framework with rule-based, vision-based, and language-based frameworks. The algorithm combinations performed excellently in document region detection, semantic mapping, and overall correctness. Cross-modal solutions can be used for real-time education. Further analysis showed the proportion of all subsystems and confirmed the necessity of reliably obtaining results from a wide range of heterogeneous and noisy data. There are limitations when dealing with charts that have different writing styles or do not follow standard drawing conventions, which may make understanding and grouping more difficult. Has it been proven that there is a reliable computational method to verify automatically high-fidelity tasks?