license: unknown
language:
- tha
pretty_name: Alice Thi
task_categories:
- optical-character-recognition
tags:
- optical-character-recognition
ALICE-THI is a Thai handwritten script dataset that contains 24045 character images, which is split into Thai handwritten character dataset (THI-C68) for 14490 images and Thai handwritten digit dataset (THI-D10) for 9555 images. The data was collected from 150 native writers aged from 20 to 23 years old. The participants were allowed to write only the isolated Thai script on the form and at least 100 samples per character. The character images obtained from this dataset generally have no background noise.
Languages
tha
Supported Tasks
Optical Character Recognition
Dataset Usage
Using datasets library
from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/alice_thi", trust_remote_code=True)
Using seacrowd library
# Load the dataset using the default config
dset = sc.load_dataset("alice_thi", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("alice_thi"))
# Load the dataset using a specific config
dset = sc.load_dataset_by_config_name(config_name="<config_name>")
More details on how to load the seacrowd library can be found here.
Dataset Homepage
https://www.ai.rug.nl/~mrolarik/ALICE-THI/
Dataset Version
Source: 1.0.0. SEACrowd: 2024.06.20.
Dataset License
Unknown (unknown)
Citation
If you are using the Alice Thi dataloader in your work, please cite the following:
@article{SURINTA2015405,
title = "Recognition of handwritten characters using local gradient feature descriptors",
journal = "Engineering Applications of Artificial Intelligence",
volume = "45",
number = "Supplement C",
pages = "405 - 414",
year = "2015",
issn = "0952-1976",
doi = "https://doi.org/10.1016/j.engappai.2015.07.017",
url = "http://www.sciencedirect.com/science/article/pii/S0952197615001724",
author = "Olarik Surinta and Mahir F. Karaaba and Lambert R.B. Schomaker and Marco A. Wiering",
keywords = "Handwritten character recognition, Feature extraction, Local gradient feature descriptor,
Support vector machine, k-nearest neighbors"
}
@article{lovenia2024seacrowd,
title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages},
author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya},
year={2024},
eprint={2406.10118},
journal={arXiv preprint arXiv: 2406.10118}
}