Datasets:

zirak-ai
/

PashtoOCR

dataset_info:
  features:
    - name: id
      dtype: int64
    - name: image
      dtype: image
    - name: text
      dtype: string
    - name: bbox
      dtype: string
    - name: lines
      dtype: string
    - name: tokens
      dtype: string
    - name: theme
      dtype: string
    - name: font
      dtype: string
    - name: font_size
      dtype: int64
    - name: font_weight
      dtype: int64
    - name: font_color
      dtype: string
    - name: line_height
      dtype: int64
    - name: bg_color
      dtype: string
    - name: justify
      dtype: string
    - name: justify_last_line
      dtype: string
  splits:
    - name: test
      num_bytes: 160016504
      num_examples: 10000
  download_size: 142306866
  dataset_size: 160016504
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*
license: mit
task_categories:
  - text-generation
language:
  - ps
  - ar
  - en
  - fa
tags:
  - ocr
  - nlp
  - cv
size_categories:
  - 10K<n<100K

PsOCR - Pashto OCR Dataset

🌐 Zirak.ai | 🤗 HuggingFace | GitHub | Kaggle | 📑 Paper

PsOCR: Benchmarking Large Multimodal Models for Optical Character Recognition in Low-resource Pashto Language

The dataset is also available at: https://www.kaggle.com/datasets/drijaz/PashtoOCR

Introduction

PsOCR is a large-scale synthetic dataset for Optical Character Recognition in low-resource Pashto language.
This is the first publicly available comprehensive Pashto OCR dataset consisting of One Million synthetic images annotated at word, line, and document-level granularity, covering extensive variations including 1000 unique font families, diverse colors, image sizes, and text layouts.
PsOCR includes the first publicly available OCR benchmark comprising 10,000 images, facilitating systematic evaluation and comparison of OCR systems for the low-resource Pashto.
We conducted a pioneering evaluation and comparison of state-of-the-art LMMs on Pashto OCR, providing crucial insights into their zero-shot capabilities, strengths, and limitations for low-resource languages written in Perso-Arabic scripts.
ℹ️ On this repo, only the test set (benchmark) is available. If you need the full dataset of 1M images, please contact us.

Performance Comparison of various LMMs on PsOCR Benchmark

Granularity

The annotation information is provided at three levels of granularity: page-level, line-level, and token-level

Font Variation

PsOCR features 1000 unique font families, a few of them are shown here.

Citation

If you found our work useful, please feel free to cite it:

@article{Haq2026PsOCR,
  title   = {PsOCR: Benchmarking Large Multimodal Models for Optical Character Recognition in Low-Resource Pashto Language},
  journal = {Ain Shams Engineering Journal},
  volume  = {17},
  number  = {3},
  pages   = {104024},
  year    = {2026},
  issn    = {2090-4479},
  doi     = {10.1016/j.asej.2026.104024},
  url     = {https://www.sciencedirect.com/science/article/pii/S2090447926000511},
  author  = {Ijazul Haq and Yingjie Zhang and Muhammad Saqib}
}

Contact

Website: https://zirak.ai/
Email Address: contact@zirak.ai, mail@ijaz.me