  Driven by deep learning and the large volume of data, scene text recognition
has evolved rapidly in recent years. Formerly, RNN-attention based methods have
dominated this field, but suffer from the problem of \textit{attention drift}
in certain situations. Lately, semantic segmentation based algorithms have
proven effective at recognizing text of different forms (horizontal, oriented
and curved). However, these methods may produce spurious characters or miss
genuine characters, as they rely heavily on a thresholding procedure operated
on segmentation maps. To tackle these challenges, we propose in this paper an
alternative approach, called TextScanner, for scene text recognition.
TextScanner bears three characteristics: (1) Basically, it belongs to the
semantic segmentation family, as it generates pixel-wise, multi-channel
segmentation maps for character class, position and order; (2) Meanwhile, akin
to RNN-attention based methods, it also adopts RNN for context modeling; (3)
Moreover, it performs paralleled prediction for character position and class,
and ensures that characters are transcripted in correct order. The experiments
on standard benchmark datasets demonstrate that TextScanner outperforms the
state-of-the-art methods. Moreover, TextScanner shows its superiority in
recognizing more difficult text such Chinese transcripts and aligning with
target characters.
