Handwritten Character Recognition from Wearable Passive RFID
This work addresses character recognition for wearable applications, but it is incremental as it builds on existing methods with a new sensor and dataset.
The paper tackles handwritten character recognition using a novel wearable electro-textile sensor panel that captures stroke order and bitmap data, achieving 72% accuracy on a dataset of 7500 characters from ten subjects.
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.