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Deep Learning Based Air-Writing Recognition with the Choice of Proper Interpolation Technique
- Source :
- Sensors, Vol 21, Iss 24, p 8407 (2021)
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- The act of writing letters or words in free space with body movements is known as air-writing. Air-writing recognition is a special case of gesture recognition in which gestures correspond to characters and digits written in the air. Air-writing, unlike general gestures, does not require the memorization of predefined special gesture patterns. Rather, it is sensitive to the subject and language of interest. Traditional air-writing requires an extra device containing sensor(s), while the wide adoption of smart-bands eliminates the requirement of the extra device. Therefore, air-writing recognition systems are becoming more flexible day by day. However, the variability of signal duration is a key problem in developing an air-writing recognition model. Inconsistent signal duration is obvious due to the nature of the writing and data-recording process. To make the signals consistent in length, researchers attempted various strategies including padding and truncating, but these procedures result in significant data loss. Interpolation is a statistical technique that can be employed for time-series signals to ensure minimum data loss. In this paper, we extensively investigated different interpolation techniques on seven publicly available air-writing datasets and developed a method to recognize air-written characters using a 2D-CNN model. In both user-dependent and user-independent principles, our method outperformed all the state-of-the-art methods by a clear margin for all datasets.
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 21
- Issue :
- 24
- Database :
- Directory of Open Access Journals
- Journal :
- Sensors
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.02b560377154bfab47435218b0c38aa
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/s21248407