Finger in Camera Speaks Everything: Unconstrained Air-Writing for Real-World
Meiqi Wu, Kaiqi Huang, Yuanqiang Cai, Shiyu Hu, Yuzhong, Zhao, Weiqiang Wang

TL;DR
This paper introduces a large video dataset and a baseline model for unconstrained air-writing recognition using RGB cameras, enabling practical human-computer interaction without complex sensors.
Contribution
It presents the first comprehensive video-based air-writing dataset covering 3,755 Chinese characters and a novel recognition model that outperforms existing methods in real-world scenarios.
Findings
The dataset contains 8.8 million frames for 3,755 characters.
The baseline model achieves superior recognition accuracy.
RGB cameras suffice for effective air-writing recognition.
Abstract
Air-writing is a challenging task that combines the fields of computer vision and natural language processing, offering an intuitive and natural approach for human-computer interaction. However, current air-writing solutions face two primary challenges: (1) their dependency on complex sensors (e.g., Radar, EEGs and others) for capturing precise handwritten trajectories, and (2) the absence of a video-based air-writing dataset that covers a comprehensive vocabulary range. These limitations impede their practicality in various real-world scenarios, including the use on devices like iPhones and laptops. To tackle these challenges, we present the groundbreaking air-writing Chinese character video dataset (AWCV-100K-UCAS2024), serving as a pioneering benchmark for video-based air-writing. This dataset captures handwritten trajectories in various real-world scenarios using commonly accessible…
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Taxonomy
TopicsDigital Games and Media
MethodsSparse Evolutionary Training
