These characters are recognized as f in all the witnesses because of similar shape.
the model trained on noisy data are unable to correct them into s. Nonetheless, by removing the factor of long s, i.e., replacing the long s in the ground truth with f, Seq2Seq-Noisy could achieve a CER of 0.062 for single-input decoding and 0.058 for multi-input decoding on the TCP books.