  The paper proposes a new text recognition network for scene-text images. Many
state-of-the-art methods employ the attention mechanism either in the text
encoder or decoder for the text alignment. Although the encoder-based attention
yields promising results, these schemes inherit noticeable limitations. They
perform the feature extraction (FE) and visual attention (VA) sequentially,
which bounds the attention mechanism to rely only on the FE final single-scale
output. Moreover, the utilization of the attention process is limited by only
applying it directly to the single scale feature-maps. To address these issues,
we propose a new multi-scale and encoder-based attention network for text
recognition that performs the multi-scale FE and VA in parallel. The
multi-scale channels also undergo regular fusion with each other to develop the
coordinated knowledge together. Quantitative evaluation and robustness analysis
on the standard benchmarks demonstrate that the proposed network outperforms
the state-of-the-art in most cases.
