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

Parallel Scale-wise Attention Network for Effective Scene Text Recognition