Create README.md

README.md

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