Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Recognition of Arabic Air-Written Letters: Machine Learning, Convolutional Neural Networks, and Optical Character Recognition (OCR) Techniques

Khalid M.O. Nahar
* ORCID logo ,
Izzat Alsmadi
ORCID logo ,
Rabia Al Mamlook
* ,
Ahmad Nasayreh
ORCID logo ,
Hasan Gharabieh
,
Ali Saeed Almuflih
ORCID logo ,
Fahad Alasim
Version 1 : Received: 25 September 2023 / Approved: 26 September 2023 / Online: 27 September 2023 (05:20:21 CEST)

A peer-reviewed article of this Preprint also exists.

Nahar, K.M.O.; Alsmadi, I.; Al Mamlook, R.E.; Nasayreh, A.; Gharaibeh, H.; Almuflih, A.S.; Alasim, F. Recognition of Arabic Air-Written Letters: Machine Learning, Convolutional Neural Networks, and Optical Character Recognition (OCR) Techniques. Sensors 2023, 23, 9475. Nahar, K.M.O.; Alsmadi, I.; Al Mamlook, R.E.; Nasayreh, A.; Gharaibeh, H.; Almuflih, A.S.; Alasim, F. Recognition of Arabic Air-Written Letters: Machine Learning, Convolutional Neural Networks, and Optical Character Recognition (OCR) Techniques. Sensors 2023, 23, 9475.

Abstract

It is a challenging problem that air-written Arabic letters has received a lot of attention in the past decades when compared to commonly spoken languages like English languages. To fill this gap, we propose a strong model that brings together machine learning (ML) and optical character recognition (OCR) methods. The model applied several ML methods, (i.e., Neural Networks (NN), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), with deep convolutional neural networks (CNNs) such as VGG16, VGG19, and SqueezeNet for effective feature extraction. Our study utilizes the AHAWP dataset, which consists of varied writing styles and variations in hand signs, to train and evaluate the model. Preprocessing systems are applied to improve data quality by reduction bias. Besides, OCR methods are combined into our model to sequestrate individual letters from continuous air-written gestures and refine recognition results. Results of this study show that the proposed model has achieved the extreme accuracy of 88.8% using NN with VGG16. This study presents an inclusive approach that combines ML, deep CNNs, and OCR methods to address the issue of Arabic in air writing recognition research.

Keywords

Arabic air writing recognition; machine learning; OCR; recognition; deep learning

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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