  Scene Text Recognition (STR) is the problem of recognizing the correct word
or character sequence in a cropped word image. To obtain more robust output
sequences, the notion of bidirectional STR has been introduced. So far,
bidirectional STRs have been implemented by using two separate decoders; one
for left-to-right decoding and one for right-to-left. Having two separate
decoders for almost the same task with the same output space is undesirable
from a computational and optimization point of view. We introduce the
bidirectional Scene Text Transformer (Bi-STET), a novel bidirectional STR
method with a single decoder for bidirectional text decoding. With its single
decoder, Bi-STET outperforms methods that apply bidirectional decoding by using
two separate decoders while also being more efficient than those methods,
Furthermore, we achieve or beat state-of-the-art (SOTA) methods on all STR
benchmarks with Bi-STET. Finally, we provide analyses and insights into the
performance of Bi-STET.
