Handwritten Character Recognition from Wearable Passive RFID

• URL: http://arxiv.org/abs/2008.02543v1
• Date: Thu, 6 Aug 2020 09:45:29 GMT
• Title: Handwritten Character Recognition from Wearable Passive RFID
• Authors: Leevi Raivio, Han He, Johanna Virkki, Heikki Huttunen
• Abstract summary: We propose a preprocessing pipeline that fuses the sequence and bitmap representations together. The data is collected from ten subjects containing altogether 7500 characters. The proposed model reaches 72% accuracy in experimental tests, which can be considered good accuracy for this challenging dataset.
• Score: 1.3190581566723918
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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