Region-Enhanced Text Localization and Hierarchical Knowledge Extraction: Toward Intelligent Educational Document Processing

This study proposes a multimodal educational assistance system to address challenges in text localization accuracy, core knowledge point coverage, long-term memory retention, and cognitive efficiency optimization within existing intelligent education systems. By integrating Region-Enhanced Text Parsing with Surya Model and PaddleOCR, the system achieves precise semantic layout analysis and text extraction, while employing an Education-Adaptive Dynamic Weight LongKey Framework to identify core educational concepts through adaptive semantic weighting. Personalized learning pathways are dynamically generated through micro-learning strategies combined with cardbased interface optimization, which considers user proficiency assessments and cognitive load balancing. The system further implements a Dynamic Multimodal Memory Enhancement mechanism driven by forgetting curve analysis, where multimodal reinforcement materials (e.g., mnemonics, diagrams) are synchronized with individual memory decay patterns. Experimental results demonstrate that the proposed system attains $98 \%$ text localization accuracy, 92% core knowledge coverage, 65% 30-day retention rate, and reduces cognitive mastery time to 500 seconds, showing significant improvements over conventional methods in educational semantic analysis and memory reinforcement.