metadata

license: mit
dataset_info:
  features:
    - name: image
      dtype: image
    - name: text
      dtype: string
configs:
  - config_name: 0-5000
    data_files: data/train_shard_000-*
  - config_name: 5000-10000
    data_files: data/train_shard_001-*
  - config_name: 10000-15000
    data_files: data/train_shard_002-*
  - config_name: 15000-20000
    data_files: data/train_shard_003-*
  - config_name: 20000-25000
    data_files: data/train_shard_004-*
  - config_name: 25000-30000
    data_files: data/train_shard_005-*
  - config_name: 30000-35000
    data_files: data/train_shard_006-*
  - config_name: 35000-40000
    data_files: data/train_shard_007-*
  - config_name: 40000-45000
    data_files: data/train_shard_008-*
  - config_name: 45000-50000
    data_files: data/train_shard_009-*
  - config_name: 50000-55000
    data_files: data/train_shard_010-*
  - config_name: 55000-60000
    data_files: data/train_shard_011-*
  - config_name: 60000-65000
    data_files: data/train_shard_012-*
  - config_name: 65000-70000
    data_files: data/train_shard_013-*
  - config_name: 70000-75000
    data_files: data/train_shard_014-*
  - config_name: 75000-80000
    data_files: data/train_shard_015-*
  - config_name: 80000-85000
    data_files: data/train_shard_016-*
  - config_name: 85000-90000
    data_files: data/train_shard_017-*
  - config_name: 90000-95000
    data_files: data/train_shard_018-*
  - config_name: 95000-100000
    data_files: data/train_shard_019-*
  - config_name: 100000-105000
    data_files: data/train_shard_020-*
  - config_name: 105000-110000
    data_files: data/train_shard_021-*
  - config_name: 110000-115000
    data_files: data/train_shard_022-*
  - config_name: 115000-120000
    data_files: data/train_shard_023-*
  - config_name: 120000-125000
    data_files: data/train_shard_024-*
  - config_name: 125000-130000
    data_files: data/train_shard_025-*
  - config_name: 130000-135000
    data_files: data/train_shard_026-*
  - config_name: 135000-140000
    data_files: data/train_shard_027-*
  - config_name: 140000-145000
    data_files: data/train_shard_028-*
  - config_name: 145000-150000
    data_files: data/train_shard_029-*
  - config_name: 150000-155000
    data_files: data/train_shard_030-*
  - config_name: 155000-160000
    data_files: data/train_shard_031-*
  - config_name: 160000-165000
    data_files: data/train_shard_032-*
  - config_name: 165000-170000
    data_files: data/train_shard_033-*
  - config_name: 170000-175000
    data_files: data/train_shard_034-*
  - config_name: 175000-180000
    data_files: data/train_shard_035-*
  - config_name: 180000-185000
    data_files: data/train_shard_036-*
  - config_name: 185000-190000
    data_files: data/train_shard_037-*
  - config_name: 190000-195000
    data_files: data/train_shard_038-*
  - config_name: 195000-200000
    data_files: data/train_shard_039-*
pretty_name: tamily 1
language:
  - ta
source_datasets:
  - sasicodes/solvari-1
task_categories:
  - image-to-text
  - image-feature-extraction
tags:
  - Vaṭṭeḻuttu
size_categories:
  - 100K<n<1M

Tamily-1: Ancient Tamil OCR Synthetic Dataset

Tamizhi "தமிழி"

Description

Repository: sasicodes/tamily-1
Point of Contact: @sasicodes

Summary

Tamily-1 is an ancient Tamil OCR synthetic dataset generated from the first 200,000 rows of Solvari-1, a large Tamil text corpus. The dataset contains rendered images of Tamil text with various augmentations and styles, making it suitable for training OCR models.

Fields

image: PNG image of rendered Tamil text
text: Original Tamil text

Data Splits

The dataset is split into shards of 5,000 samples each, named as train_shard_XXX.

Annotation process

Each text is rendered with:

Random paper style (Palm Leaf, Pale Palm Leaf, Red Stone, White Stone, Paper)
Random background style (No Lines, With Lines, Blurred, With Lines and Noise)
Random augmentation (Rotation, Perspective, Stain, Ink Bleed)

License

MIT License

@misc{tamily-1,
  author = {sasicodes},
  title = {Tamily-1: Ancient Tamil OCR Synthetic Dataset},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face Hub},
  howpublished = {\url{https://huggingface.co/datasets/sasicodes/tamily-1}}
}