Air Writing: Recognizing Multi-Digit Numeral String Traced in Air Using RNN-LSTM Architecture

Article Type

Research Article

Publication Title

SN Computer Science


Air writing provides a more natural and immersive way of interacting with devices, with the potential of having significant application in fields like augmented reality and education. However, such systems often rely on expensive hardware, making them less accessible for general purposes. In this study, we propose a robust and inexpensive system for the recognition of multi-digit numerals traced in an air-writing environment which uses only a generic device camera for input. We employ a sliding window-based algorithm to isolate a small segment of the input for processing. A dual network configuration consisting of RNN-LSTM networks are used for noise elimination and digit recognition. We conduct our experiments on English numerals using the MNIST dataset as the baseline model to allow easy adaptability of our method. Our results are further improved by the use of Pendigits and ISI-Air online datasets. We observed a drop in accuracy with increase in the number of digits owing to the accumulation of transition noise. However, bi-directional scanning considerably reduces the impact of such noise on the recognition accuracy. Under standard conditions, our system produced an accuracy of 98.75% and 85.27% for single and multi-digit English numerals, respectively. Incorporation of selective frame skipping in the sliding window algorithm resulted in a 60% reduction in computational time, significantly improving the system performance. We provide a link to the source code of our system at the end of this paper.



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