论文标题
可穿戴无源RFID的手写角色识别
Handwritten Character Recognition from Wearable Passive RFID
论文作者
论文摘要
在本文中，我们研究了新型可穿戴电纺丝传感器面板捕获的数据的识别。依次收集数据，以便我们记录中风顺序和结果位图。我们提出了一个预处理管道，将序列和位图表示融合在一起。数据是从包含7500个字符的十个主题中收集的。我们还提出了一个卷积神经网络结构，尽管输入大小仅为10x10像素，但其新颖的上采样结构仍可以成功使用常规成像网的验证网络。在实验测试中，提出的模型达到了72 \％的准确性，对于这个具有挑战性的数据集，可以认为这是良好的准确性。数据和模型都向公众发布。
In this paper we study the recognition of handwritten characters from data captured by a novel wearable electro-textile sensor panel. The data is collected sequentially, such that we record both the stroke order and the resulting bitmap. We propose a preprocessing pipeline that fuses the sequence and bitmap representations together. The data is collected from ten subjects containing altogether 7500 characters. We also propose a convolutional neural network architecture, whose novel upsampling structure enables successful use of conventional ImageNet pretrained networks, despite the small input size of only 10x10 pixels. The proposed model reaches 72\% accuracy in experimental tests, which can be considered good accuracy for this challenging dataset. Both the data and the model are released to the public.
