A large-scale benchmark dataset for Urdu script optical character recognition with systematic augmentation

Urdu is spoken by over 230 million people worldwide, yet it remains significantly underrepresented in digital resources, with limited availability of large-scale, publicly accessible training datasets for optical character recognition (OCR). The diversity of Urdu font styles encountered in printed books, newspapers, and digital publications poses a substantial barrier to developing generalizable OCR systems, while the absence of standardized benchmarks hinders fair and reproducible comparison across recognition approaches. This data article presents FIPU-OCR-CHAR, a benchmark dataset of printed Urdu characters encompassing 48 classes: 38 alphabets and 10 numerals in their isolated forms. The dataset was constructed through a fully systematic pipeline comprising five sequential stages: font collection and validation, character set definition, base image rendering, augmentation, and dataset organization with split generation. Each character class was rendered from 201 distinct Urdu TrueType/OpenType font files, producing 9,648 base images (201 fonts × 48 classes). Each base image was subsequently processed through 34 augmentation operations encompassing geometric transforms, photometric adjustments, blur filters, noise injection, and morphological operations, producing 328,032 augmented images. The complete dataset totals 337,680 labeled PNG images, each stored at 28×28 pixel resolution with 24-bit color depth. The dataset is organized into three predefined splits: training (70%; 236,376 images), validation (20%; 67,536 images), and testing (10%; 33,768 images), each accompanied by a CSV annotation file mapping image filenames to integer class labels (0-47). The repository additionally contains a Jupyter Notebook implementing a ResNet-34 baseline classification pipeline, a results summary image, and a README file documenting dataset structure and label definitions. The dataset is publicly available on Mendeley Data under a CC BY 4.0 license and is intended for use in OCR model development, font-invariant classifier training, Urdu script digitization, transfer learning for word- and line-level recognition, and benchmarking of convolutional neural network and Vision Transformer architectures on low-resource script character recognition tasks.