Multiple attentional aggregation network for handwritten Dongba character recognition

Abstract

Handwritten Dongba character recognition (HDCR) is a challenging task due to different recognition granularity and it plays an important role in history and philology. Recently, deep learning-based methods are adopted and outperform traditional approaches. However, they do not well explore the multi-granularity information, leading to many highly similar Dongba characters cannot be recognized accurately. In this paper, a Multiple Attentional Aggregation Network (MAAN) is proposed for HDCR. Specifically, a novel Hybrid Attentional Mapping Unit (HAMU) is designed for multi-scale and richer feature expression. The hybrid attention mechanism in HAMU can enhance the discrimination between important and redundant features. Besides, a novel Spatial Attentional Aggregation Unit (SAAU) is adopted to aggregate multi-scale features of different depths. In this way, more details from large scale features and global semantic information from small scale features will be adequately aggregated. Experiments are conducted on our constructed novel Dongba character image dataset and validate the effectiveness and superiority of our methods and the extensive experimental results demonstrate that our proposed MAAN outperforms state-of-the-art algorithms.