  In the last decades, scene text recognition has gained worldwide attention
from both the academic community and actual users due to its importance in a
wide range of applications. Despite achievements in optical character
recognition, scene text recognition remains challenging due to inherent
problems such as distortions or irregular layout. Most of the existing
approaches mainly leverage recurrence or convolution-based neural networks.
However, while recurrent neural networks (RNNs) usually suffer from slow
training speed due to sequential computation and encounter problems as
vanishing gradient or bottleneck, CNN endures a trade-off between complexity
and performance. In this paper, we introduce SAFL, a self-attention-based
neural network model with the focal loss for scene text recognition, to
overcome the limitation of the existing approaches. The use of focal loss
instead of negative log-likelihood helps the model focus more on low-frequency
samples training. Moreover, to deal with the distortions and irregular texts,
we exploit Spatial TransformerNetwork (STN) to rectify text before passing to
the recognition network. We perform experiments to compare the performance of
the proposed model with seven benchmarks. The numerical results show that our
model achieves the best performance.
