  Nowadays, scene text recognition has attracted more and more attention due to
its various applications. Most state-of-the-art methods adopt an
encoder-decoder framework with attention mechanism, which generates text
autoregressively from left to right. Despite the convincing performance, the
speed is limited because of the one-by-one decoding strategy. As opposed to
autoregressive models, non-autoregressive models predict the results in
parallel with a much shorter inference time, but the accuracy falls behind the
autoregressive counterpart considerably. In this paper, we propose a Parallel,
Iterative and Mimicking Network (PIMNet) to balance accuracy and efficiency.
Specifically, PIMNet adopts a parallel attention mechanism to predict the text
faster and an iterative generation mechanism to make the predictions more
accurate. In each iteration, the context information is fully explored. To
improve learning of the hidden layer, we exploit the mimicking learning in the
training phase, where an additional autoregressive decoder is adopted and the
parallel decoder mimics the autoregressive decoder with fitting outputs of the
hidden layer. With the shared backbone between the two decoders, the proposed
PIMNet can be trained end-to-end without pre-training. During inference, the
branch of the autoregressive decoder is removed for a faster speed. Extensive
experiments on public benchmarks demonstrate the effectiveness and efficiency
of PIMNet. Our code will be available at https://github.com/Pay20Y/PIMNet.
