Below, we explore different ways to back off when the ScRNN predicts UNK (a frequent outcome for rare and unseen words):

Pass-through: word-recognizer passes on the (possibly misspelled) word as is.

Backoff to neutral word: Alternatively, noting that passing $\colorbox {gray!20}{\texttt {UNK}}$ -predicted words through unchanged exposes the downstream model to potentially corrupted text, we consider backing off to a neutral word like `a', which has a similar distribution across classes.

Backoff to background model: We also consider falling back upon a more generic word recognition model trained upon a larger, less-specialized corpus whenever the foreground word recognition model predicts UNK. Figure 1 depicts this scenario pictorially. 
 Question: How do the backoff strategies work?
In pass-through, the recognizer passes on the possibly misspelled word, backoff to neutral word backs off to a word with similar distribution across classes and backoff to background model backs off to a more generic word recognition model trained with larger and less specialized corpus.