DTRNet：用于手写中文文本识别及伪造字符检测的双文本-偏旁解码框架
DTRNet: Dual Text-Radical Decoding for Handwritten Chinese Text Recognition with Faked Character Detection
AI总结：
针对教育场景中手写中文文本识别需检测伪造字符的需求，本文提出DTRNet框架，通过解耦文本识别与结构验证，实现高效且可解释的伪造字符检测，效果显著。
AI中文摘要：
在K-12教育场景中，手写中文文本识别不仅需要转录学生书写内容，还需检测伪造字符。然而现有识别模型通常局限于预定义的正常字符集合，无法明确识别伪造字符；现有检测方法存在互补性局限：字符级方法可提供可解释的结构证据但效率较低，行级方法效率高但高度依赖置信度分数，易出现漏检且缺乏明确的结构证据。因此，关键挑战在于在保持行级效率的同时保留独立于上下文推理的字符结构证据。为此，本文提出DTRNet，一种用于行级伪造字符检测的双文本-偏旁解码框架。DTRNet将上下文感知的文本识别与逐字符结构验证解耦：文本分支执行行级转录，偏旁分支预测合法的表意字符描述序列（IDS）以用于基于词典的伪造字符判断。我们进一步引入IDS引导的置信度调整（IGCA），在推理阶段利用结构证据优化文本预测。实验结果表明，DTRNet在保持强识别性能的同时可有效检测伪造字符，并提供可解释的偏旁级证据。代码、检查点及处理后的数据集可在该httpsURL获取。
英文摘要：
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.
