Domain adaptation has shown promising advances for alleviating domain shift problem. However, recent visual domain adaptation works usually focus on non-sequential object recognition with a global coarse alignment, which is inadequate to transfer effective knowledge for sequence-like text images with variable-length fine-grained character information. In this paper, we develop a Sequence-toSequence Domain Adaptation Network (SSDAN) for robust text image recognition, which could exploit unsupervised sequence data by an attention-based sequence encoderdecoder network. In the SSDAN, a gated attention similarity (GAS) unit is introduced to adaptively focus on aligning the distribution of the source and target sequence data in an attended character-level feature space rather than a global coarse alignment. Extensive text recognition experiments show the SSDAN could efficiently transfer sequence knowledge and validate the promising power of the proposed model towards real world applications in various recognition scenarios, including the natural scene text, handwritten text and even mathematical expression recognition.