Handwritten Character Recognition from Wearable Passive RFID
Leevi Raivio, Han He, Johanna Virkki, Heikki Huttunen

TL;DR
This paper presents a novel wearable RFID-based system for recognizing handwritten characters by combining sequence and bitmap data, using a specialized CNN architecture, achieving 72% accuracy on a new dataset.
Contribution
It introduces a new wearable electro-textile sensor, a data fusion preprocessing pipeline, and a CNN with a novel upsampling structure for small input size recognition.
Findings
Achieved 72% accuracy on the dataset
Collected 7500 characters from 10 subjects
Released data and model publicly
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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Taxonomy
TopicsHand Gesture Recognition Systems · Handwritten Text Recognition Techniques · Biometric Identification and Security
