  Scene text recognition (STR) is still a hot research topic in computer vision
field due to its various applications. Existing works mainly focus on learning
a general model with a huge number of synthetic text images to recognize
unconstrained scene texts, and have achieved substantial progress. However,
these methods are not quite applicable in many real-world scenarios where 1)
high recognition accuracy is required, while 2) labeled samples are lacked. To
tackle this challenging problem, this paper proposes a few-shot adversarial
sequence domain adaptation (FASDA) approach to build sequence adaptation
between the synthetic source domain (with many synthetic labeled samples) and a
specific target domain (with only some or a few real labeled samples). This is
done by simultaneously learning each character's feature representation with an
attention mechanism and establishing the corresponding character-level latent
subspace with adversarial learning. Our approach can maximize the
character-level confusion between the source domain and the target domain, thus
achieves the sequence-level adaptation with even a small number of labeled
samples in the target domain. Extensive experiments on various datasets show
that our method significantly outperforms the finetuning scheme, and obtains
comparable performance to the state-of-the-art STR methods.
