  Scene text recognition is a popular topic and extensively used in the
industry. Although many methods have achieved satisfactory performance for the
close-set text recognition challenges, these methods lose feasibility in
open-set scenarios, where collecting data or retraining models for novel
characters could yield a high cost. For example, annotating samples for foreign
languages can be expensive, whereas retraining the model each time when a novel
character is discovered from historical documents costs both time and
resources. In this paper, we introduce and formulate a new open-set text
recognition task which demands the capability to spot and recognize novel
characters without retraining. A label-to-prototype learning framework is also
proposed as a baseline for the proposed task. Specifically, the framework
introduces a generalizable label-to-prototype mapping function to build
prototypes (class centers) for both seen and unseen classes. An open-set
predictor is then utilized to recognize or reject samples according to the
prototypes. The implementation of rejection capability over out-of-set
characters allows automatic spotting of unknown characters in the incoming data
stream. Extensive experiments show that our method achieves promising
performance on a variety of zero-shot, close-set, and open-set text recognition
datasets
