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arXiv · 2008.02543

Handwritten Character Recognition from Wearable Passive RFID

Leevi Raivio·Han He·Johanna Virkki·Heikki Huttunen
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Abstract

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.

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Leevi Raivio, Han He, Johanna Virkki, Heikki Huttunen. 2020-08-06. Handwritten Character Recognition from Wearable Passive RFID. https://arxiv.org/abs/2008.02543

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