amrosama/al-kawakib-magazine-ocr
Al-Kawakib Magazine OCR Pages This dataset contains rendered Arabic magazine page images paired with page-level text and line-level bounding boxes. It is intended for OCR, document understanding, and VLM fine-tuning experiments. Fine-Tuning Notebook A standalone Google Colab notebook for DeepSeek-OCR 3B + TRL SFT is available here: Open the fine-tuning notebook in Colab The notebook can train on this dataset alone or on all three Arabic magazine OCR… See the full description on the dataset page: https://huggingface.co/datasets/amrosama/al-kawakib-magazine-ocr.
Al-Kawakib Magazine OCR Pages
<p align="center"> <img src="assets/previewtextpage.jpg" alt="Split preview with OCR bounding boxes and extracted text" width="70%"> </p>
This dataset contains rendered Arabic magazine page images paired with page-level text and line-level bounding boxes. It is intended for OCR, document understanding, and VLM fine-tuning experiments.
Fine-Tuning Notebook
A standalone Google Colab notebook for DeepSeek-OCR 3B + TRL SFT is available here:
Open the fine-tuning notebook in Colab
The notebook can train on this dataset alone or on all three Arabic magazine OCR datasets.
Dataset Structure
Each row represents one page image.
Splits
Split mode: volume.
Total rows: 44834
Usage
import json
from datasets import load_dataset
from huggingface_hub import get_token
ds = load_dataset(
"amrosama/al-kawakib-magazine-ocr",
)
sample = next(iter(ds["train"]))
image = sample["image"]
text = sample["ocr_text"]
line_texts = sample["line_texts"]
lines = json.loads(sample["lines_json"])
bboxes = json.loads(sample["line_bboxes"])
print(sample["image_id"], image.size, sample["line_count"])For VLM fine-tuning, use image as the visual input, instruction as the prompt, and response as the target answer.
For non-streaming use on machines with enough disk/cache space:
from datasets import load_dataset
from huggingface_hub import get_token
ds = load_dataset("amrosama/al-kawakib-magazine-ocr", token=get_token())
sample = ds["train"][0]Curation Notes
Images without matching valid OCR JSON were skipped. Common source/footer marks and angled watermark fragments were filtered from the text fields; the source page images are unchanged. The text and bounding boxes were generated automatically and may still contain mistakes from historical print quality, page skew, stamps, low contrast, cropped text, or mixed page layouts.
Suggested Evaluation
Use held-out pages to evaluate full-page transcription quality, line detection coverage, and robustness across different publication years and scan conditions.
License & Attribution
This dataset is released under the [CC BY 4.0 License](https://creativecommons.org/licenses/by/4.0/).
In plain English: You are completely free to use, share, adapt, or fine-tune models with this dataset for any purpose—academic or commercial. The only requirement is that you give credit by mentioning or citing this dataset.
Citation
If you use this dataset in your work or research, please cite it as:
@misc{al_kawakib_ocr_dataset,
author = {Amr Osama Abdellatif},
title = {Al-Kawakib Magazine OCR Dataset},
year = {2026},
publisher = {Hugging Face},
journal = {Hugging Face Repository},
howpublished = {\url{[https://huggingface.co/datasets/amrosama/al-kawakib-magazine-ocr](https://huggingface.co/datasets/amrosama/al-kawakib-magazine-ocr)}}
}