Adaptive Feature Aggregation for In-Air Handwritten Trajectory

As a novel human-computer interaction modality, in-air handwriting character recognition has drawn the attention of researchers. Nevertheless, existing camera-based in-air handwriting character recognition algorithms, such as those employing average pooling or CTC decoding, overlook the varying importance of features, leading to relatively low recognition accuracy. In this study, we present an adaptive feature aggregation approach. Our method can allocate weights based on the significance of features in the time dimension and perform aggregation in accordance with these weights. Multiple experiments demonstrate that our module surpasses existing methods in terms of performance. We have achieved remarkable enhancements in recognition accuracy on the AWCV-100K-UCAS2024, and WiTA datasets. Our project is open-sourced at: https://github.com/Mayo001/AFA.