ArXiv

Persian Pixel: A large-scale synthetic OCR dataset for Persian language

2026-07-22 · 17:13 UTC ·Pouria Mahdi, Haq Nawaz Malik ·1 min read
Authors
Pouria Mahdi, Haq Nawaz Malik
Categories
cs.CV, cs.AI
arXiv
https://arxiv.org/abs/2607.20385v1
PDF
https://arxiv.org/pdf/2607.20385v1

Brief

Persian Pixel is a large-scale synthetic OCR dataset introduced by Pouria Mahdi and Haq Nawaz Malik (arXiv 2026-07-22) containing >343k image–text pairs synthesized from a 7M-word Persian corpus with the SynthOCR-Gen renderer. The generation faithfully models Persian script complexities (cursive joining, contextual glyphs, ligatures, diacritics, Naskh/Nastaliq) and applies 25+ stochastic degradation models to reduce the synthetic-to-real gap. The authors position the open dataset as a cost-effective resource for training transformer OCRs (TrOCR, Donut) and accelerating Persian document digitization; this summary is based on the abstract (full paper not reviewed).

Why it matters

Persian Pixel (Mahdi & Malik, arXiv 2026-07-22) is a synthetic OCR dataset of over 343,000 high-fidelity image–text pairs generated from a curated 7-million-word Persian corpus using the SynthOCR-Gen rendering framework.

Key details

  • The pipeline models Persian-specific typography (obligatory cursive joining, positional glyph variants, extensive ligatures, diacritics, Naskh and Nastaliq typefaces) and augments images with more than 25 stochastic degradations (ink bleed, paper aging, blur, illumination variation, scanner imperfections, compression, noise).
  • Dataset is intended to train and fine-tune modern transformer-based OCRs (e.g., TrOCR, Donut) to address Persian OCR scarcity for ~110 million speakers and to provide a scalable, open alternative to costly manual annotation.
Source evidence

Abstract

Optical Character Recognition (OCR) for Persian remains substantially less mature than for Latin-script languages despite Persian being spoken by more than 110 million people across multiple countries. This gap arises from two fundamental challenges: the intrinsic complexity of the Perso-Arabic writing system and the limited availability of large-scale, high-quality annotated datasets. Persian script exhibits obligatory cursive connectivity, context-dependent glyph shaping, extensive ligatures, diacritic placement, and stylistic variation across writing forms such as Naskh and Nastaliq, all of which significantly complicate text recognition. At the same time, the high cost and labor-intensive nature of manual annotation have created a persistent data bottleneck, limiting the development of robust OCR systems and slowing progress in Persian document digitization.In this paper, we introduce Persian Pixel, a comprehensive synthetic OCR dataset specifically designed to address these challenges. Comprising over 343,000 high-fidelity image text pairs, the dataset spans sentence, paragraph, and full-page document layouts generated from a carefully curated seven-million-word Persian corpus using the SynthOCR-Gen rendering framework. The generation pipeline faithfully models the typographic characteristics of Persian script, including contextual character joining, positional glyph variants, diacritic placement, and multiple representative Persian typefaces. To bridge the synthetic-to-real domain gap, the rendered images are further enriched with more than twenty-five stochastic degradation models that emulate realistic document acquisition artifacts, including ink bleed, paper aging, blur, illumination variation, scanner imperfections, compression artifacts, and multiple noise processes.By overcoming the long-standing scarcity of annotated Persian OCR data, Persian Pixel provides a scalable and openly available resource for training and fine-tuning modern OCR architectures, including transformer-based models such as TrOCR and Donut. The dataset establishes a strong foundation for research in Persian document analysis, historical manuscript digitization, and end-to-end document understanding, while demonstrating that programmatic synthetic data generation offers a practical, cost-effective, and scalable alternative to manual annotation for advancing OCR in low-resource and typographically complex scripts.