  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.
