--- # For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/datasets-cards {} --- # Dataset Card for Arabic Characters ## Dataset Details ### Dataset Description This dataset contains 16,800 Arabic handwritten characters, written by 60 participants. It is intended for Arabic character recognition tasks using machine learning. The dataset is split into a training set of 13,440 images and a test set of 3,360 images, with 28 Arabic characters (labeled 0–27). Each image is 32×32 pixels in grayscale, scanned at 300 dpi and preprocessed. The original source is the Arabic Handwritten Characters Dataset. - **License:** Open Database License (ODbL) ### Dataset Sources - **Homepage:** https://github.com/mloey/Arabic-Handwritten-Characters-Dataset - **Paper:** El-Sawy, A., Loey, M., & El-Bakry, H. (2017). Arabic handwritten characters recognition using convolutional neural network. WSEAS Transactions on Computer Research, 5(1), 11-19. ## Dataset Structure Total images: 16,800 Splits: - **Train**: 13,440 images (80%) - **Test**: 3,360 images (20%) Classes (labels): 28 (Arabic letters), labeled 0–27 Image specs: PNG format, 32×32 pixels, grayscale ## Example Usage Below is a quick example of how to load this dataset via the Hugging Face Datasets library. ``` from datasets import load_dataset # Load the dataset dataset = load_dataset("randall-lab/arabic-characters", split="train", trust_remote_code=True) # dataset = load_dataset("randall-lab/arabic-characters", split="test", trust_remote_code=True) # Access a sample from the training set example = dataset["train"][0] image = example["image"] label = example["label"] image.show() # Display the image print(f"Label: {label}") ``` ## Citation **BibTeX:** @article{el2017arabic, title={Arabic handwritten characters recognition using convolutional neural network}, author={El-Sawy, Ahmed and Loey, Mohamed and El-Bakry, Hazem}, journal={WSEAS Transactions on Computer Research}, volume={5}, pages={11--19}, year={2017} }