A Deep Learning-Powered TinyML Model for Gesture-Based Air Handwriting Simple Arabic Letters Recognition
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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 [21]
Cites
A Comprehensive Survey on TinyML
Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366 · see citing works
A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification
Rokh, B., Azarpeyvand, A., & Khanteymoori, A. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3623402 · Cited by 254 · see citing works
Arabic handwriting recognition system using convolutional neural network
Altwaijry, Ν., & Al-Turaiki, I. M. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05070-8 · Cited by 229 · see citing works
Chen, M., AlRegib, G., & Juang, B.-H. F. (2015) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2015.2492598 · Cited by 158 · see citing works
TinyML: A Systematic Review and Synthesis of Existing Research
Han, H., & Siebert, J. (2022) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic54071.2022.9722636 · Cited by 117 · see citing works
TinyML: Current Progress, Research Challenges, and Future Roadmap
Shafique, M., Theocharides, T., Reddy, V. J., et al. (2021) · doi:10.1109/dac18074.2021.9586232 · Cited by 115 · see citing works
Supervised Machine Learning: A Survey
Mrabet, M. A. E., Makkaoui, K. E., & Faize, A. (2021) · International Conference on Advanced Communication Technologies and Networking (CommNet) · doi:10.1109/commnet52204.2021.9641998 · Cited by 74 · see citing works
Ghanim, T. M., Khalil, M. I., & Abbas, H. M. (2020) · IEEE Access · doi:10.1109/access.2020.2994290 · Cited by 69 · see citing works
Air-writing recognition using smart-bands
Yanay, T., & Shmueli, E. (2020) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2020.101183 · Cited by 58 · see citing works
Air-Writing Recognition Based on Deep Convolutional Neural Networks
Hsieh, C., Lo, Y.-S., Chen, J., & Tang, S.-K. (2021) · IEEE Access · doi:10.1109/access.2021.3121093 · Cited by 51 · see citing works
A lexicon-free approach for 3D handwriting recognition using classifier combination
Kumar, P., Saini, R., Roy, P. P., & Pal, U. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.12.014 · Cited by 38 · see citing works
Lamaakal, I., Ouahbi, I., El Makkaoui, K., Maleh, Y., Pławiak, P., & Alblehai, F. (2024) · IEEE Access · doi:10.1109/access.2024.3406631 · Cited by 34 · see citing works
Self-citationA TinyML Model for Gesture-Based Air Handwriting Arabic Numbers Recognition
Lamaakal, I., Makkaoui, K. E., Ouahbi, I., & Maleh, Y. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.05.070 · Cited by 33 · see citing works
Self-citationDeep Learning Based Air-Writing Recognition with the Choice of Proper Interpolation Technique
Abir, F. A., Siam, M. M. H. K., Sayeed, A., Hasan, M. A. M., & Shin, J. (2021) · Sensors · doi:10.3390/s21248407 · Cited by 28 · see citing works
Smart Gesture-based Control in Human Computer Interaction Applications for Special-need People
Rady, M. A., Youssef, S. M., & Fayed, S. F. (2019) · Novel Intelligent and Leading Emerging Sciences Conference (NILES) · doi:10.1109/niles.2019.8909324 · Cited by 15 · see citing works
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Lamaakal, I., Maleh, Y., El Makkaoui, K., Ouahbi, I., Pławiak, P., Alfarraj, O., Almousa, M., & Abd El-Latif, A. A. (2025) · Sensors · doi:10.3390/s25051318 · Cited by 60 · see citing works
Self-citationLamaakal, I., Yahyati, C., Maleh, Y., El Makkaoui, K., Ouahbi, I., El-Latif, A. A. A., Zomorodi, M., & El-Rahiem, B. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-26297-2 · Cited by 26 · see citing works
Self-citationEfficient Gesture-Based Recognition of Tifinagh Characters in Air Handwriting with a TinyDL Model
Lamaakal, I., El Mourabit, N., El Makkaoui, K., Ouahbi, I., & Maleh, Y. (2024) · 2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS) · doi:10.1109/icds62089.2024.10756483 · Cited by 14 · see citing works
Self-citation
- Authors
- Lamaakal, I., Maleh, Y., Ouahbi, I., El Makkaoui, K., & Abd El-Latif, A. A.
- Affiliations
- Mohamed I University (MA)
- Université Sultan Moulay Slimane (MA)
- Corresponding author
- Lamaakal, Ismail
- Venue
- Lecture Notes in Networks and Systems
- Publisher
- Springer Nature Switzerland
- Published
- 2024
- Pages
- 32-42
- ISSN
- 2367-3370 (ISSN-L)
- 2367-3389
- OpenAlex
- W4402096033
- Cited by
- 20
- References
- 15
- Citations by year
- 2026: 3 · 2025: 16 · 2024: 1
- FWCI
- 6.44
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in networks and systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- air handwriting, Arabic letter recognition, gesture recognition, TinyML, convolutional neural networks, real-time deployment, gesture-based input
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