Towards Open-Set Text Recognition via Label-to-Prototype Learning
AbstractScene text recognition is a popular topic and can benefit various tasks.
Although many methods have been proposed for the close-set text
recognition challenges, they cannot be directly applied to open-set
scenarios, where the evaluation set contains novel characters not
appearing in the training set. Conventional methods require collecting
new data and retraining the model to handle these novel characters,
which is an expensive and tedious process. In this paper, we propose a
label-to-prototype learning framework to handle novel characters without
retraining the model. In the proposed framework, novel characters are
effectively mapped to their corresponding prototypes with a
label-to-prototype learning module. This module is trained on characters
with seen labels and can be easily generalized to novel characters.
Additionally, feature-level rectification is conducted via
topology-preserving transformation, resulting in better alignments
between visual features and constructed prototypes while having a
reasonably small impact on model speed. A lot of experiments show that
our method achieves promising performance on a variety of zero-shot,
close-set, and open-set text recognition datasets.