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license: cc-by-sa-4.0 |
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task_categories: |
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- image-classification |
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- image-feature-extraction |
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- tabular-classification |
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- feature-extraction |
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language: |
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- ar |
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- en |
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pretty_name: 'HDL: Hand-drawn Digital Logic Gates Dataset' |
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--- |
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📌 Dataset Summary |
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HDL (Hand-drawn Digital Logic Gates) is the first balanced image and tabular dataset dedicated to the classification of hand-drawn digital logic gates. The dataset is designed primarily for machine learning education, computer vision research, and AI-assisted electronics applications. |
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HDL contains 1,200 hand-drawn images covering the 8 fundamental digital logic gates (AND, OR, NOT (INVERTER), BUFFER, NAND, NOR, XOR, and XNOR), with 150 images per class, ensuring perfect class balance. |
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🧠 Motivation |
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Most existing handwritten or hand-drawn datasets focus on: |
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* Digits (e.g., MNIST) |
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* Characters and scripts |
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However, no publicly available balanced dataset existed for hand-drawn digital logic gates, despite their importance in: |
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* Digital electronics education |
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* Circuit analysis |
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* AI-assisted schematic interpretation |
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HDL was created to fill this gap and to support: |
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* Teaching Machine Learning to engineering students |
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* No-code ML experimentation |
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* Benchmarking shallow and deep learning models |
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📂 Images Dataset Structure |
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``` |
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HDL/ |
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├── AND/ |
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├── OR/ |
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├── NOT/ |
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├── BUFFER/ |
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├── NAND/ |
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├── NOR/ |
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├── XOR/ |
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└── XNOR/ |
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``` |
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* Image format: `.jpg` / `.jpeg` |
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* Image source: Hand-drawn on paper and photographed or scanned |
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* Contributors: Umm Al-Qura University students + logic gates drawn using Logisim-evolution |
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📊 Benchmark Results |
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The dataset has been extensively benchmarked using both no-code tools and deep learning models. |
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🔹 Best Result |
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* Model: EfficientNet with data augmentation |
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* Platform: Liner.ai |
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* Accuracy: 92.5% |
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* F1-score: 92.5% |
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🔹 Feature Extraction (Image Embedding) (Tabular Dataset) |
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Image embeddings were generated using: |
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* SqueezeNet |
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* Inception v3 |
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* Google ViT |
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* Facebook DINOv2 |
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* Nvidia MambaVision |
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Top-performing classifiers: |
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* Logistic Regression |
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* Neural Networks |
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🧪 Typical Use Cases |
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HDL is suitable for: |
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🎓 Machine Learning Education |
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🧠 TinyML & Edge AI |
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✍️ OCR-like systems for circuit digitization |
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🧩 Logic gate classification |
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🖼️ Image embedding & feature extraction experiments |
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🛠️ No-code ML platforms (Orange Data Mining, Liner.ai) |
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📄 Research Paper: To be published soon. |
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Please cite the dataset if used in academic work. |
