KHATT-Arabic Dataset
KHATT-Arabic Dataset
Datasets
KHATT-Arabic Dataset
File
KHATT-Arabic Dataset
Use Case
Computer Vision
Description
Explore the KHATT-Arabic Dataset, a unique collection of unconstrained handwritten Arabic texts from 1,000 diverse writers.
KHATT-Arabic, developed through collaborative efforts led by Professor Sabri Mahmoud at King Fahd University of Petroleum and Minerals (KFUPM) in Dhahran, Saudi Arabia, in partnership with Professor Fink of TU-Dortmund, Germany, and Dr. Märgner of TU-Braunschweig, Germany, is a comprehensive database for unconstrained Arabic handwriting research. This dataset consists of handwritten Arabic texts created by 1,000 diverse writers, making it a rich resource for various handwriting recognition studies including, but not limited to, text recognition and writer identification.
Dataset Features:
Contributors: Handwritten forms from 1,000 unique writers.
Resolution Quality: Images scanned at 200, 300, and 600 DPI to accommodate different research needs.
Diversity: Contributors vary by nationality, age, gender, handedness, and educational background.
Writing Styles: Includes natural, unrestricted handwriting styles.
Content Variety:
Unique Texts: 2,000 paragraphs on varied topics such as arts, education, health, nature, and technology, along with their line-segmented images.
Similar Texts: 2,000 paragraphs covering all Arabic characters and shapes, each with line-segmented images.
Free Texts: Paragraphs on topics freely chosen by the writers.
Annotation: All paragraph and line images come with manually verified ground truths and Latin transliterations of Arabic texts.
Dataset Splits: The dataset is organized into training (70%), validation (15%), and testing (15%) sets.
Research Applications:
The KHATT-Arabic dataset is designed to support advancements in several areas of handwriting analysis, including writer identification, line segmentation, noise removal, and binarization techniques, in addition to general handwritten text recognition.
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