In K-12 educational scenarios, handwritten Chinese text recognition should not only transcribe student writing, but also detect faked characters. However, existing recognition models are usually confined to a predefined set of normal characters and therefore cannot explicitly identify faked characters. Existing detection methods exhibit complementary limitations: character-level methods provide interpretable structural evidence but suffer from low efficiency, whereas line-level methods are efficient but rely heavily on confidence scores, making them prone to missed detections and lacking explicit structural evidence. Thus, the key challenge is to preserve character-structural evidence independent of contextual inference while maintaining line-level efficiency. To this end, we propose DTRNet, a dual Text-Radical decoding framework for line-level faked character detection. DTRNet decouples context-aware text recognition from character-wise structural verification, where the text branch performs line-level transcription and the radical branch predicts legal Ideographic Description Sequences (IDS) for lexicon-based faked character judgment. We further introduce IDS-Guided Confidence Adjustment (IGCA) to refine text predictions using structural evidence during inference. Experimental results demonstrate that DTRNet effectively detects faked characters while maintaining strong recognition performance and providing interpretable radical-level evidence. Code, checkpoints, and the processed dataset are publicly available at https://github.com/BNU-ERC-ITEA/DTRNet.
Handwritten text recognition (HTR) in examination scenarios has gained increasing attention for its role in intelligent grading systems. However, existing studies have not systematically modeled the complex handwriting phenomena inherent in exam settings, hindering a comprehensive understanding of the recognition challenges and limitations of current methods. Specifically, handwriting artifacts pose significant challenges to recognition models in two complementary aspects: sequentially, they disrupt the reading order and lead to non-monotonic sequences, while visually, they distort character structures and induce attention drift. To enable systematic benchmarking of exam handwriting, we first construct BNU-Exam-HTR, a large-scale dataset of handwritten exam text, and establish BNU-Exam-Benchmark, a fine-grained evaluation framework defining 12 representative challenges observed in real exam handwriting. To overcome these challenges, we further propose EduOCR, a recognition model with a collaborative dual-branch decoder. The Sequential Symbol Module (SSM) uses autoregressive decoding to handle non-monotonic sequences, while the Permutation-Aware Prediction Head (PPH) simulates artifact perturbations to guide the shared encoder in distinguishing characters from noise, thus stabilizing attention and mitigating alignment errors. Extensive experiments show that EduOCR consistently outperforms state-of-the-art HTR models, OCR tools, and multimodal large language models across all 12 challenges, demonstrating superior robustness and adaptability.
Runrui Li, Lin Zhu, Hua Huang· IEEE Transactions on Pattern...· 0 citations
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