--- license: cc-by-nd-4.0 language: - fa task_categories: - image-to-text - text-to-image tags: - ocr - synthetic - persian - farsi - naskh - document-ai pretty_name: Persian Pixel size_categories: - 100K |

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| ## Dataset summary | Property | Value | |---|---| | Language | Persian / Farsi (`fa`) | | Script | Perso-Arabic | | Modality | Image → Text (OCR) | | Source | Synthetic (font-rendered) | | Total rows (`full`) | 343,246 | | License | CC BY-ND 4.0 | ## Configurations and sizes The dataset is organized by render granularity. Each granularity is exposed as its own config, plus a `full` config that virtually concatenates all three. | Config | Description | Rows | Typical width | |---|---|---:|---| | `sentence` | Short line / sentence-level crops |251,000 | 640 px | | `paragraph` | Multi-line paragraph blocks | 110,138 | 512 px | | `page` | Page-level renders | 31,108 | 768 px | | `full` | Concatenation of all three configs above | 343,246 | — | Verified from Parquet shard metadata on **2026-06-25 05:34:30 UTC** (320 Parquet files, ~31.8 GB). ### Page-config statistics | Metric | Min | Max | Mean | |---|---:|---:|---:| | Height (px) | 38 | 1100 | 597.92 | | Text length (chars) | 13 | 1896 | 824.22 | | Line count | 2 | 75 | 7.88 | ## Data fields Every row shares the same schema across all configs: | Field | Type | Description | |---|---|---| | `bytes` | `bytes` | Encoded image bytes | | `path` | `string` | Image path/name stored with the image bytes | | `text` | `string` | Exact Persian Unicode OCR target / transcription | | `sample_type` | `string` | One of `sentence`, `paragraph`, or `page` | | `source_run_id` | `string` | Internal generation / publish run identifier | | `image_path` | `string` | Original relative image path | | `width` | `int` | Rendered image width in pixels | | `height` | `int` | Rendered image height in pixels | | `text_chars` | `int` | Number of Unicode characters in `text` | | `line_count` | `int` | Number of text lines in the label | ## Usage Install the dependency: ```bash pip install datasets pillow ``` Load a single config: ```python from datasets import load_dataset train = load_dataset("Omarrran/Persian_Pixel", "sentence", split="train") # or "paragraph" / "page" print(train.column_names) print(train[0]["text"]) ``` Load the combined config: ```python from datasets import load_dataset full = load_dataset("Omarrran/Persian_Pixel", "full", split="train") print(len(full)) # 343246 ``` Stream a config (recommended for the heavier `paragraph` / `page` renders): ```python from datasets import load_dataset ds = load_dataset( "Omarrran/Persian_Pixel", name="page", split="train", streaming=True, ) for sample in ds: txt = sample["text"] # feed into OCR preprocessing / tokenizer pipeline ``` If your loader sees raw `bytes` and `path` columns rather than a decoded `image` object, reconstruct a PIL image: ```python from io import BytesIO from PIL import Image row = train[0] img = Image.open(BytesIO(row["bytes"])) print(img.size, row["text"]) ``` ### Loading each config ```python from datasets import load_dataset sentence = load_dataset("Omarrran/Persian_Pixel", "sentence", split="train") paragraph = load_dataset("Omarrran/Persian_Pixel", "paragraph", split="train") page = load_dataset("Omarrran/Persian_Pixel", "page", split="train") full = load_dataset("Omarrran/Persian_Pixel", "full", split="train") ``` ## Recommended training split strategy The repo publishes data as `train` shards. For model development, create your own deterministic split: ```python ds = load_dataset("Omarrran/Persian_Pixel", "sentence", split="train") split = ds.train_test_split(test_size=0.02, seed=42) train_ds, val_ds = split["train"], split["test"] ``` For page-level OCR, keep validation smaller and representative because page images are much heavier than sentence crops. ## Recommended use cases - Fine-tuning image-to-text and OCR models such as TrOCR, Donut, BLIP-2, and PaliGemma-style decoders. - Pretraining or curriculum stages before introducing noisy scanned or photographed OCR corpora. - Evaluating normalization, decoding, and post-correction pipelines on Perso-Arabic script. - Benchmarking Persian text recognition under controlled synthetic conditions and variable line counts. ## Limitations and considerations - This is **synthetic** OCR data. Performance on font-rendered text does not guarantee performance on real scans, photographs, or handwriting — validate on in-domain data before any production deployment. - Synthetic rendering can introduce artifacts, unusual line wrapping, punctuation variation, or source-text noise. - The `paragraph` and `page` configs contain long, multi-line labels and large images; always consume them through the `datasets` library rather than raw parsing. ## License Released under **Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0)**. You may copy and redistribute the dataset for any purpose with attribution, but you may not distribute modified versions. ## Citation ```bibtex @dataset{persian_pixel_2026, title = {Persian Pixel: A Synthetic OCR Dataset for Persian/Farsi}, author = { Haq Nawaz Malik, Pouria Mahdi}, year = {2026}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/Omarrran/Persian_Pixel} } ``` ## Repository - Dataset repo: https://huggingface.co/datasets/Omarrran/Persian_Pixel - Format: Parquet shards - Primary task: Persian OCR / image-to-text