Efficient Gesture-Based Recognition of Tifinagh Characters in Air Handwriting with a TinyDL Model
Abstract
Gesture-based recognition of Tifinagh characters in air handwriting presents a novel approach to intuitive input methods, particularly beneficial for users in regions where Tifinagh scripts are prevalent. In this paper, we propose an efficient solution leveraging TinyDL (Tiny Deep Learning) models to achieve remarkable accuracy in character recognition. Our TinyDL model, developed through rigorous experimentation and refinement, achieves an impressive accuracy of 97.8%. Furthermore, we address the challenge of model size by implementing advanced pruning techniques, resulting in a drastic reduction from the original 648KB model to a compact 53KB TinyDL model. This significant compression ensures efficient deployment and utilization, particularly in resource-constrained environments such as mobile devices and embedded systems. Our findings underscore the potential of TinyDL models in enabling high-performance recognition systems while mitigating the overhead associated with model size, paving the way for practical deployment of gesture-based Tifinagh character recognition solutions.
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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 [22]
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Self-citation
- Authors
- Lamaakal, I., El Mourabit, N., El Makkaoui, K., Ouahbi, I., & Maleh, Y.
- Affiliations
- Mohamed I University (MA)
- Mohammed Premier University,Multidisciplinary Faculty of Nador,Oujda,Morocco
- Université Sultan Moulay Slimane (MA)
- Venue
- 2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS)
- Publisher
- IEEE
- Published
- 2024-10-23
- Pages
- 1-8
- OpenAlex
- W4404688733
- Cited by
- 14
- References
- 34
- Citations by year
- 2026: 2 · 2025: 12
- FWCI
- 4.51
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Intelligent Computing in Data Sciences (ICDS) (publishedVersion)
- 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
- Tifinagh script, air handwriting, model compression, pruning, resource-constrained deployment, handwritten character recognition
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