  Scene text recognition has attracted a great many researches due to its
importance to various applications. Existing methods mainly adopt recurrence or
convolution based networks. Though have obtained good performance, these
methods still suffer from two limitations: slow training speed due to the
internal recurrence of RNNs, and high complexity due to stacked convolutional
layers for long-term feature extraction. This paper, for the first time,
proposes a no-recurrence sequence-to-sequence text recognizer, named NRTR, that
dispenses with recurrences and convolutions entirely. NRTR follows the
encoder-decoder paradigm, where the encoder uses stacked self-attention to
extract image features, and the decoder applies stacked self-attention to
recognize texts based on encoder output. NRTR relies solely on self-attention
mechanism thus could be trained with more parallelization and less complexity.
Considering scene image has large variation in text and background, we further
design a modality-transform block to effectively transform 2D input images to
1D sequences, combined with the encoder to extract more discriminative
features. NRTR achieves state-of-the-art or highly competitive performance on
both regular and irregular benchmarks, while requires only a small fraction of
training time compared to the best model from the literature (at least 8 times
faster).
