Air-Writing Recognition using Deep Convolutional and Recurrent Neural Network Architectures
| Autor: | Grigoris Bastas, Kosmas Kritsis, Vassilis Katsouros |
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| Rok vydání: | 2020 |
| Předmět: |
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Artificial neural network Computer science business.industry Deep learning 010401 analytical chemistry Pattern recognition 02 engineering and technology 01 natural sciences Convolutional neural network 0104 chemical sciences Recurrent neural network Gesture recognition 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Feature learning Curse of dimensionality |
| Zdroj: | ICFHR |
| Popis: | In this paper, we explore deep learning architectures applied to the air-writing recognition problem where a person writes text freely in the three dimensional space. We focus on handwritten digits, namely from 0 to 9, which are structured as multidimensional time-series acquired from a Leap Motion Controller (LMC) sensor. We examine both dynamic and static approaches to model the motion trajectory. We train and compare several state-of-the-art convolutional and recurrent architectures. Specifically, we employed a Long Short-Term Memory (LSTM) network and also its bidirectional counterpart (BLSTM) in order to map the input sequence to a vector of fixed dimensionality, which is subsequently passed to a dense layer for classification among the targeted air-handwritten classes. In the second architecture we adopt 1D Convolutional Neural Networks (CNNs) to encode the input features before feeding them to an LSTM neural network (CNN-LSTM). The third architecture is a Temporal Convolutional Network (TCN) that uses dilated causal convolutions. Finally, a deep CNN architecture for automating the feature learning and classification from raw input data is presented. The performance evaluation has been carried out on a dataset of 10 participants, who wrote each digit at least 10 times, resulting in almost 1200 examples. |
| Databáze: | OpenAIRE |
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