# AirScript - Creating Documents in Air

## TL;DR

AirScript introduces a real-time, gesture-based document creation system in air that recognizes handwritten characters using deep learning, providing visual feedback and high accuracy in various environments.

## Contribution

The paper presents a novel algorithm 2-DifViz for converting air hand movements into visualized 2D coordinates and a deep learning recognition module combining CNN and GRUs, advancing gesture-based document creation.

## Key findings

- Achieved 91.7% accuracy in person-independent recognition.
- Achieved 96.7% accuracy in person-dependent recognition.
- Outperformed traditional models like HMM, KNN, SVM in accuracy.

## Abstract

This paper presents a novel approach, called AirScript, for creating, recognizing and visualizing documents in air. We present a novel algorithm, called 2-DifViz, that converts the hand movements in air (captured by a Myo-armband worn by a user) into a sequence of x, y coordinates on a 2D Cartesian plane, and visualizes them on a canvas. Existing sensor-based approaches either do not provide visual feedback or represent the recognized characters using prefixed templates. In contrast, AirScript stands out by giving freedom of movement to the user, as well as by providing a real-time visual feedback of the written characters, making the interaction natural. AirScript provides a recognition module to predict the content of the document created in air. To do so, we present a novel approach based on deep learning, which uses the sensor data and the visualizations created by 2-DifViz. The recognition module consists of a Convolutional Neural Network (CNN) and two Gated Recurrent Unit (GRU) Networks. The output from these three networks is fused to get the final prediction about the characters written in air. AirScript can be used in highly sophisticated environments like a smart classroom, a smart factory or a smart laboratory, where it would enable people to annotate pieces of texts wherever they want without any reference surface. We have evaluated AirScript against various well-known learning models (HMM, KNN, SVM, etc.) on the data of 12 participants. Evaluation results show that the recognition module of AirScript largely outperforms all of these models by achieving an accuracy of 91.7% in a person independent evaluation and a 96.7% accuracy in a person dependent evaluation.