gfbati/HDL

📌 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.