  Texts from scene images typically consist of several characters and exhibit a
characteristic sequence structure. Existing methods capture the structure with
the sequence-to-sequence models by an encoder to have the visual
representations and then a decoder to translate the features into the label
sequence. In this paper, we study text recognition framework by considering the
long-term temporal dependencies in the encoder stage. We demonstrate that the
proposed Temporal Convolutional Encoder with increased sequential extents
improves the accuracy of text recognition. We also study the impact of
different attention modules in convolutional blocks for learning accurate text
representations. We conduct comparisons on seven datasets and the experiments
demonstrate the effectiveness of our proposed approach.
