Persian Pixel: A Large-Scale Synthetic OCR Dataset for Persian Language
Score
75填补了波斯语大规模合成OCR数据集的空白，提供了具体的生成方法和定量规模，信息增量显著。
数据集构建方法严谨，生成管线考虑了波斯文字的多重复杂性，退化模型丰富，方法描述完整。
为波斯语OCR研究和应用提供了可直接使用的资源，有利于推动相关领域的技术进步。
当前OCR领域对低资源语言关注度上升，该数据集发布及时，具有现实需求。
来源为arXiv预印本，作者提供了完整的技术细节和数据集公开承诺，可信度高。
Base 83.5 × T2 Source weight ×0.9 = 75
Curation line 70Selected
Why selected
This work fills a critical gap in Persian OCR datasets with large scale, rigorous design, and rich degradation models, offering high practical value; however, generalization to real-world scenarios remains to be fully validated.
AI commentary
「This paper is highly relevant for researchers working on multilingual OCR, especially for low-resource complex scripts. It addresses the persistent data bottleneck for Persian OCR via programmatic synthesis, and the methodology is transferable to similar scripts like Arabic and Urdu. If you are building or improving non-Latin OCR systems, the degradation strategies and generation pipeline here offer a solid reference framework.」
Quick overview
Persian OCR lags behind due to script complexity and data scarcity. This paper presents Persian Pixel, a dataset of over 343,000 high-fidelity image-text pairs covering sentences, paragraphs, and full pages, generated from a 7-million-word Persian corpus using SynthOCR-Gen. The pipeline faithfully models Persian typography and applies 25+ stochastic degradations (e.g., ink bleed, paper aging, blur) to bridge the synthetic-to-real gap. This scalable, openly available resource supports training modern OCR architectures like TrOCR and Donut, advancing Persian document analysis and demonstrating programmatic data generation as a cost-effective alternative for low-resource scripts.
- Persian Pixel comprises 343,000+ image-text pairs spanning sentences, paragraphs, and full pages.
- Generated from a 7-million-word Persian corpus using SynthOCR-Gen, faithfully modeling complex Persian script ligatures and glyph variants.
- Applies 25+ stochastic degradation models (ink bleed, paper aging, blur, etc.) to mimic real document artifacts.
- Suitable for training and fine-tuning modern OCR architectures like TrOCR and Donut, a breakthrough for low-resource complex-script OCR.
Full text
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