A TinyDL Model for Gesture-Based Air Handwriting Arabic Numbers and Simple Arabic Letters Recognition
Open accessAbstract
The application of tiny machine learning (TinyML) in human-computer interaction is revolutionizing gesture recognition technologies. However, there remains a significant gap in the literature regarding the effective recognition of complex scripts, such as Arabic, in real-time applications. This research aims to bridge this gap by leveraging TinyML for the accurate recognition of Arabic numbers and simple letters through gesture-based air handwriting. For the first time, we introduce a novel tiny deep learning (TinyDL) model that utilizes a lightweight convolutional neural network (CNN) architecture specifically designed to handle the intricacies of the Arabic script and adaptable for the TinyML domain. Despite the widespread use of CNNs in gesture recognition, our model stands out by achieving an exceptional accuracy rate of 97.5% in decoding 2D gesture inputs of Arabic numerals and letters. This high level of accuracy demonstrates the effectiveness of our TinyDL model in addressing the unique challenges posed by Arabic script recognition, thereby making it a user-friendly and accessible solution. Moreover, our research contributes to the advancement of TinyML applications in real-world gesture recognition apps, showcasing the potential of TinyML in transforming the interaction between humans and digital devices.
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TLMs are typically created by employing techniques such as knowledge distillation, pruning, quantization, and specialized architectures designed to minimize model size and memory footprint.
“TLMs are typically created by employing techniques such as knowledge distillation, pruning, quantization [17,18,19,20,21,22,23], and specialized architectures [24] designed to minimize model size and memory footprint.”
From Tiny Language Models for Automation and Control: Overview, Potential Applications, and Future Research Directions (2025) · cites this paper as [20]
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A Review of TinyML for Human Activity Recognition on Edge Devices
Lamaakal, I., Yahyati, C., Maleh, Y., Makkaoui, K. E., & Ouahbi, I. (2026) · Machine Learning and Knowledge Extraction · doi:10.3390/make8090271 · Cited by 1 · see citing works
Self-citation
- Authors
- Lamaakal, I., Ouahbi, I., El Makkaoui, K., Maleh, Y., Pławiak, P., & Alblehai, F.
- Affiliations
- Mohamed I University (MA)
- Multidisciplinary Faculty of Nador, University Mohammed Premier, Oujda, Morocco
- Université Sultan Moulay Slimane (MA)
- Cracow University of Technology (PL)
- King Saud University (SA)
- Venue
- IEEE Access
- Published
- 2024
- Volume
- 12
- Pages
- 76589-76605
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4399073794
- Semantic Scholar
- c2e10984cc10bbf8b4f4864b7bcdcd817127decd
- Cited by
- 34
- References
- 63
- Citations by year
- 2026: 11 · 2025: 20 · 2024: 3
- FWCI
- 10.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Access (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- King Saud University, Riyadh, Saudi Arabia, through the Researchers Supporting Project, (RSPD2024R564)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Handwritten Text Recognition Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- TinyML, gesture recognition, air handwriting, Arabic numeral recognition, Arabic letter recognition, lightweight convolutional neural network, Arabic script recognition, human-computer interaction, real-time recognition
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