  Scene Text Recognition (STR) is difficult because of the variations in text
styles, shapes, and backgrounds. Though the integration of linguistic
information enhances models' performance, existing methods based on either
permuted language modeling (PLM) or masked language modeling (MLM) have their
pitfalls. PLM's autoregressive decoding lacks foresight into subsequent
characters, while MLM overlooks inter-character dependencies. Addressing these
problems, we propose a masked and permuted implicit context learning network
for STR, which unifies PLM and MLM within a single decoder, inheriting the
advantages of both approaches. We utilize the training procedure of PLM, and to
integrate MLM, we incorporate word length information into the decoding process
and replace the undetermined characters with mask tokens. Besides, perturbation
training is employed to train a more robust model against potential length
prediction errors. Our empirical evaluations demonstrate the performance of our
model. It not only achieves superior performance on the common benchmarks but
also achieves a substantial improvement of $9.1\%$ on the more challenging
Union14M-Benchmark.
