Leveraging the Image Processing Tools and Techniques in Enhancing the Efficiency of Hand Gesture and Digital Recognition System

Shreya Bhardwaj


Vol 12, Jul-Dec, 2020

Date of Submission: 2020-07-28 Date of Acceptance: 2020-09-08 Date of Publication: 2020-10-03


The ability to bridge the communication gap between people with speech and hearing impairments is a benefit of sign language. Gestures and postures are combined to create sign language. The natural method of communication is gestures, which are signs made with or without an object. People with disabilities could communicate their feelings without an interpreter, thanks to the sign language system. A novel webcam-based hand gesture recognition system for two-digit classification is proposed in this paper. This system's basic concept is to convert gestures from digital images or devices that capture images into text. Due to individual variations in hand colour, size, and shape, gesture-based communication is very complicated. The data glove-based method and hand-attached motion sensors are two examples of various approaches to gesture recognition. The hand's position and movement are tracked by these sensors. An appropriate set of equipment is required for the sensor-based method. It is expensive and may impede the hand's natural movement. A rapid and dependable vision-based method should be developed to overcome the limitations of data glove-relied approaches. The proposed system utilized the live feed of image processing and machine learning's learning capabilities to solve the recognition problem.


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