This paper presents a novel approach to the digital signing of electronic documents through the use of a camera-based interaction system, single-finger tracking for sign recognition, and multi commands executing hand gestures. The proposed solution, referred to as "Air Signature," involves writing the signature in front of the camera, rather than relying on traditional methods such as mouse drawing or physically signing on paper and showing it to a web camera. The goal is to develop a state-of-the-art method for detecting and tracking gestures and objects in real-time. The proposed methods include applying existing gesture recognition and object tracking systems, improving accuracy through smoothing and line drawing, and maintaining continuity during fast finger movements. An evaluation of the fingertip detection, sketching, and overall signing process is performed to assess the effectiveness of the proposed solution. The secondary objective of this research is to develop a model that can effectively recognize the unique signature of a user. This type of signature can be verified by neural cores that analyze the movement, speed, and stroke pixels of the signing in real time. The neural cores use machine learning algorithms to match air signatures to the individual's stored signatures, providing a secure and efficient method of verification. Our proposed System does not require sensors or any hardware other than the camera.

翻译：本文提出了一种基于摄像头交互系统、单指跟踪签名识别以及多指令手势执行的电子文档数字签名新方法。所提出的解决方案称为“空中签名”，即在摄像头前书写签名，而非采用鼠标绘制或纸质签名后展示给摄像头等传统方式。研究目标在于开发一种实时检测与跟踪手势及物体的前沿方法。具体方案包括：应用现有手势识别与物体跟踪系统，通过平滑处理与线条绘制提升精度，并确保快速手指运动时的连续性。通过对指尖检测、草图绘制及整体签名过程的评估，验证了该方案的有效性。本研究的次要目标是发展一种能够有效识别用户独特签名的模型。此类签名可通过神经核心进行验证，这些神经核心能实时分析签名动作的运动轨迹、速度及笔画像素。神经核心采用机器学习算法将空中签名与用户存储的签名进行匹配，从而实现安全高效的验证方法。所提出的系统除摄像头外无需任何传感器或额外硬件。