ocr_data

Synthetic Arabic document images with layout annotations, for OCR training.

Layout

WebDataset .tar shards. Files sharing a basename are one sample, so the image becomes the png/jpg column and the annotation the json column.

data/shard_001.tar ... data/shard_103.tar                 originals (PNG + JSON)
data_aug/shard_001_aug1.tar ... data_aug/shard_103_aug3.tar   augmented variants (JPEG + JSON)

Each shard holds up to 9990 samples (~1 GB). data/ and data_aug/ are separate so you can train on clean originals alone.

Loading

from datasets import load_dataset

# one shard
ds = load_dataset("webdataset",
                  data_files="hf://datasets/OCR-Data/ocr_data/data/shard_001.tar",
                  split="train", streaming=True)

# a range of shards
ds = load_dataset("webdataset",
                  data_files="hf://datasets/OCR-Data/ocr_data/data/shard_{001..010}.tar",
                  split="train", streaming=True)

# everything, originals + augmented
ds = load_dataset("webdataset", data_files={"train": [
        "hf://datasets/OCR-Data/ocr_data/data/*.tar",
        "hf://datasets/OCR-Data/ocr_data/data_aug/*.tar"]},
      split="train", streaming=True)

meta.augmentation in each annotation is null for originals and {"name": ..., "params": {...}} for augmented variants.