Air Handwriting using AI and Ml

Air Handwriting using AI and Ml

Shital Patil1, Homesh Chaudhari2, Homeshwari Chaudhari3, Komal Kapase 4, Saurabh Shinde5

*1 Assistant Professor, Department of Information Technology, Sir Visvesvaraya Institute of Technology, Nashik, Maharashtra, India

*2,3,4,5 Department of Information Technology, Sir Visvesvaraya Institute of Technology, Nashik, Maharashtra, India

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Abstract - Air-writing recognition has received wide attention due to its potential application in intelligent systems. To date, some of the fundamental problems in isolated writing have not been addressed effectively. This paper presents a simple yet effective air-writing recognition approach based on deep convolutional neural networks (CNNs). Radiational air-writing requires an extra device containing sensor(s), while the wide adoption of smart-bands eliminates the requirement of the extra device. Therefore, air-writing recognition systems are becoming more flexible day by day. However, the variability of signal duration is a key problem in developing an air-writing recognition model. Inconsistent signal duration is obvious due to the nature of the writing and data-recording process.

Key Words: air-writing recognition, interpolation, time-series data, convolutional neural network (CNN), hand tracking.