Effective Gesture Based Framework for Capturing User Input

@article{Charan2022EffectiveGB,
  title={Effective Gesture Based Framework for Capturing User Input},
  author={Pabbathi Sri Charan and Saksham Gupta and Satvik Agrawal and G. S. Sindhu},
  journal={ArXiv},
  year={2022},
  volume={abs/2208.00913}
}
. Computers today aren't just confined to laptops and desktops. Mobile gadgets like mobile phones and laptops also make use of it. However, one input device that hasn't changed in the last 50 years is the QWERTY keyboard. Users of virtual keyboards can type on any surface as if it were a keyboard thanks to sensor technology and artificial intelligence. In this research, we use the idea of image processing to create an application for seeing a computer keyboard using a novel framework which can… 
View PDF on arXiv

Figures and Tables from this paper

References

SHOWING 1-10 OF 21 REFERENCES

Gesture Recognition Based Virtual Mouse and Keyboard

Here the Convex hull defects are first generated and then using the defect calculations an algorithm is generated and mapping the mouse and keyboard functions with the defects, the computer will understand the gesture shown by the user and act accordingly.

Virtual Mouse using Hand Gestures

This study presents a method for controlling the cursor’s position without the need of any electronic equipment, and actions such as clicking and dragging things will be carried out using various hand gestures.

Human-Computer Interaction Using Manual Hand Gestures in Real Time

The construction of an electronic system that can recognise twelve manual motions made by an interlocutor with one of their hands in a situation with regulated lighting and background in real time is described.

Using Depth Cameras for Recognition and Segmentation of Hand Gestures

How infrared camera images can be used to segment, classify, and recognise one-handed gestures in a variety of lighting conditions is looked at.

Real time finger tracking and contour detection for gesture recognition using OpenCV

  • R. GuravP. Kadbe
  • Computer Science
    2015 International Conference on Industrial Instrumentation and Control (ICIC)
  • 2015
This paper proposes a context-free grammar based proposed method that gives effective real time performance with great accuracy and robustness for more than four hand gestures and implements the alternate representation method for same gestures i.e. fingertip detection using convex hull algorithm.

Hand Gesture-Based Character Recognition Using OpenCV and Deep Learning

This work left the detection, tracking and drawing tasks on mathematics-based algorithms like Accumulated Weight, CSRT (The Channel and Spatial Reliability) Tracker and OpenCV (Open Computer Vision) library, and its deep learning model is 98.56% accurate in classifying symbols which is more accurate than previous methods while not requiring any special sensors.

Interactive manipulation of 3D objects using Kinect for visualization tools in education

The developed Kinect-based 3D gesture recognition system detects and tracks human hands from the RGBD images captured by a Kinect sensor and recognizes human gestures by counting the number of open fingers of each fist and tracking 3D motion of both hands.

Classification of Soft Keyboard Typing Behaviors Using Mobile Device Sensors with Machine Learning

A system to classify users’ typing behaviors based on the data produced by the built-in sensors and can distinguish the device owner’s typing behavior from those of others with 100% accuracy is proposed.

Hand gesture recognition on python and opencv

This project concentrates on how a system could detect, recognize and interpret the hand gesture recognition through computer vision with the challenging factors which variability in pose, orientation, location and scale.

Real-time static custom gestures recognition based on skeleton hand

The proposed approach for real-time static gestures recognition based on the skeleton of a hand using a MediaPipe framework and Support Vector Machine classification can be extended for dynamic gesture recognition and used to control robots and computers.