  Text recognition is a popular topic for its broad applications. In this work,
we excavate the implicit task, character counting within the traditional text
recognition, without additional labor annotation cost. The implicit task plays
as an auxiliary branch for complementing the sequential recognition. We design
a two-branch reciprocal feature learning framework in order to adequately
utilize the features from both the tasks. Through exploiting the complementary
effect between explicit and implicit tasks, the feature is reliably enhanced.
Extensive experiments on 7 benchmarks show the advantages of the proposed
methods in both text recognition and the new-built character counting tasks. In
addition, it is convenient yet effective to equip with variable networks and
tasks. We offer abundant ablation studies, generalizing experiments with deeper
understanding on the tasks. Code is available.
