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.
@article{arxiv.2008.02543,
title = {Handwritten Character Recognition from Wearable Passive RFID},
author = {Leevi Raivio and Han He and Johanna Virkki and Heikki Huttunen},
journal= {arXiv preprint arXiv:2008.02543},
year = {2020}
}