---
title: "Persian Pixel：面向波斯语的大规模合成OCR数据集"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.20385v1"
permalink: "https://feed.astralor.com/items/itm_8e2ede5343585eb32fe91c9b"
published: "2026-07-22T17:13:25.000Z"
score: 75
channel: "ai"
tags: ["Donut","Persian Pixel","SynthOCR-Gen","TrOCR","光学字符识别","合成数据生成"]
---

# Persian Pixel：面向波斯语的大规模合成OCR数据集

## AI 点评

> 这篇论文对从事多语言OCR、特别是低资源复杂文字的研究者来说非常值得关注。它通过程序化合成解决了长期困扰波斯语OCR的数据瓶颈，而且方法具有可迁移性——类似思路可以适配到阿拉伯语、乌尔都语等同类文字。如果你在构建或改进非拉丁文字OCR系统，Persian Pixel 的退化策略和生成管线提供了很好的参考框架。

## 速览

波斯语OCR因文字复杂性和标注数据稀缺而发展滞后。本文提出Persian Pixel数据集，包含超过34.3万对高保真图像-文本对，覆盖句子、段落和整页布局，基于700万词波斯语语料库使用SynthOCR-Gen框架生成。生成流程精确模拟波斯语排版特性，并引入25种随机退化模型（如墨迹渗散、纸张老化、模糊）以弥合合成与真实域差距。该数据集可扩展、免费获取，支持TrOCR、Donut等现代OCR架构训练，为波斯语文档分析和历史手稿数字化奠定基础，证明程序化数据生成是低资源、复杂文字OCR的实用替代方案。

## 关键点

- Persian Pixel 包含 343,000+ 图像-文本对，覆盖句子、段落和整页。
- 基于 700 万词波斯语语料库，使用 SynthOCR-Gen 框架生成，精确模拟波斯文字复杂连接和变体。
- 引入 25 种以上随机退化模型（墨迹渗散、纸张老化、模糊等）以模拟真实文档采集瑕疵。
- 适用于 TrOCR、Donut 等现代 OCR 架构的训练与微调，是低资源复杂文字 OCR 的重要突破。

## 全文

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.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_8e2ede5343585eb32fe91c9b