Recently, handwritten Chinese character error correction has been greatly improved by employing encoder-decoder methods to decompose a Chinese character into an ideographic description sequence (IDS). However, existing methods implicitly capture and encode linguistic information inherent in IDS sequences, leading to a tendency to generate IDS sequences that match seen characters. This poses a challenge when dealing with an unseen misspelled character, as the decoder may generate an IDS sequence that matches a seen character instead. Therefore, we introduce Count, Decode and Fetch (CDF), a novel approach that exhibits better generalization towards unseen misspelled characters. CDF is mainly composed of three parts: the counter, the decoder, and the fetcher. In the first stage, the counter predicts the number of each radical class without the symbol-level position annotations. In the second stage, the decoder employs the counting information and generates the IDS sequence step by step. Moreover, by updating the counting information at each time step, the decoder becomes aware of the existence of each radical. With the decomposed IDS sequence, we can determine whether the given character is misspelled. If it is misspelled, the fetcher under the transductive transfer learning strategy predicts the ideal character that the user originally intended to write. We integrate our method into existing encoder-decoder models and significantly enhance their performance.

最近，采用编码器-解码器方法将汉字分解为表意描述序列（IDS）的手写汉字纠错得到显著改进。然而，现有方法隐含地捕捉和编码IDS序列中固有的语言信息，导致生成与已见字符匹配的IDS序列的倾向。鉴于此，我们引入了Count，Decode和Fetch（CDF），一种新颖的方法，对于未见过的错误拼写字符具有更好的泛化性。通过集成到现有的编码器-解码器模型中，我们显著提高了其性能。

计数、解码和提取：一种新的手写中文字符纠错方法