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Air-GR: An Over-the-Air Handwritten Character Recognition System Based on Coordinate Correction YOLOv5 Algorithm and

Yajun Zhang1, Zijian Li1, Zhixiong Yang1

  • 1School of Software Engineering, Xinjiang University, Ürümqi 830046, China.

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|February 11, 2023
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Summary

This study introduces Air-GR, a novel contactless gesture recognition system for fast and accurate text input. Air-GR uses computer vision and a lightweight neural network to recognize handwritten characters from gesture trajectories with 95.24% accuracy.

Keywords:
Air-GRLGR-CNNYOLOv5gesture coordinate correction algorithmgesture recognitiontime window algorithm

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Area of Science:

  • Human-Computer Interaction
  • Computer Vision
  • Machine Learning

Background:

  • Traditional input devices like mice and keyboards limit interaction speed and naturalness.
  • Computer vision-based contactless gesture recognition is a growing research area.
  • Existing gesture recognition systems struggle with limited gesture sets and slow, inaccurate text input.

Purpose of the Study:

  • To develop an over-the-air handwritten character recognition system (Air-GR) for faster and more natural human-computer interaction.
  • To overcome the limitations of current computer vision-based gesture recognition, particularly for text input.
  • To improve the accuracy and efficiency of recognizing complex gestures.

Main Methods:

  • Proposed a novel system, Air-GR, utilizing a coordinate correction YOLOv5 algorithm and a lightweight convolutional neural network (LGR-CNN).
  • Developed a method to generate gesture images from trajectory points rather than direct image capture.
  • Implemented a time-window-based algorithm for segmenting gesture coordinates and a gesture coordinate correction algorithm to enhance detection accuracy.

Main Results:

  • The LGR-CNN achieved higher accuracy in gesture trajectory image classification compared to VGG16, ResNet, and GoogLeNet.
  • The Air-GR system demonstrated the ability to quickly and effectively recognize combinations of 26 English letters and numbers.
  • Achieved a high recognition accuracy of 95.24% for the proposed gesture recognition system.

Conclusions:

  • Air-GR offers a significant advancement in contactless gesture recognition for efficient text input.
  • The system effectively addresses the limitations of existing gesture recognition technologies.
  • The proposed methods for gesture detection, segmentation, and recognition pave the way for more intuitive human-computer interfaces.