  Scene Text Recognition is a challenging problem because of irregular styles
and various distortions. This paper proposed an end-to-end trainable model
consists of a finer rectification module and a bidirectional attentional
recognition network(Firbarn). The rectification module adopts finer grid to
rectify the distorted input image and the bidirectional decoder contains only
one decoding layer instead of two separated one. Firbarn can be trained in a
weak supervised way, only requiring the scene text images and the corresponding
word labels. With the flexible rectification and the novel bidirectional
decoder, the results of extensive evaluation on the standard benchmarks show
Firbarn outperforms previous works, especially on irregular datasets.
