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Upload alice_thi.py with huggingface_hub
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alice_thi.py
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+
# coding=utf-8
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+
# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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+
#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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+
# limitations under the License.
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+
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from pathlib import Path
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+
from typing import Dict, List, Tuple
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+
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import datasets
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+
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from seacrowd.utils import schemas
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from seacrowd.utils.configs import SEACrowdConfig
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+
from seacrowd.utils.constants import TASK_TO_SCHEMA, Licenses, Tasks
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+
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+
_CITATION = """\
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+
@article{SURINTA2015405,
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+
title = "Recognition of handwritten characters using local gradient feature descriptors",
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+
journal = "Engineering Applications of Artificial Intelligence",
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+
volume = "45",
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+
number = "Supplement C",
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+
pages = "405 - 414",
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+
year = "2015",
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+
issn = "0952-1976",
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+
doi = "https://doi.org/10.1016/j.engappai.2015.07.017",
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+
url = "http://www.sciencedirect.com/science/article/pii/S0952197615001724",
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author = "Olarik Surinta and Mahir F. Karaaba and Lambert R.B. Schomaker and Marco A. Wiering",
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+
keywords = "Handwritten character recognition, Feature extraction, Local gradient feature descriptor,
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+
Support vector machine, k-nearest neighbors"
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+
}
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+
"""
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+
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+
_DATASETNAME = "alice_thi"
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+
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+
_DESCRIPTION = """\
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ALICE-THI is a Thai handwritten script dataset that contains 24045 character
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images, which is split into Thai handwritten character dataset (THI-C68) for
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47 |
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14490 images and Thai handwritten digit dataset (THI-D10) for 9555 images. The
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48 |
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data was collected from 150 native writers aged from 20 to 23 years old. The
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49 |
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participants were allowed to write only the isolated Thai script on the form and
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at least 100 samples per character. The character images obtained from this
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51 |
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dataset generally have no background noise.
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+
"""
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+
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_HOMEPAGE = "https://www.ai.rug.nl/~mrolarik/ALICE-THI/"
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+
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_LANGUAGES = ["tha"]
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_SUBSETS = {
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"THI-D10": {
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"data_dir": "Thai_digit_sqr",
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+
"label_dict": {
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0: "0",
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1: "1",
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2: "2",
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3: "3",
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4: "4",
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5: "5",
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6: "6",
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7: "7",
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8: "8",
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9: "9",
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},
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},
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"THI-C68": {
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"data_dir": "Thai_char_sqr",
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"label_dict": {
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0: "ko kai",
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1: "kho khai",
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2: "kho khuat",
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3: "kho khwai",
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4: "kho khon",
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5: "kho rakhang",
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6: "ngo ngu",
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7: "cho chan",
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8: "cho ching",
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9: "cho chang",
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10: "so so",
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11: "cho choe",
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12: "yo ying",
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13: "do chada",
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14: "to patak",
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15: "tho than",
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16: "tho nangmontho",
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17: "tho phuthao",
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18: "no nen",
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19: "do dek",
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20: "to tao",
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21: "tho thung",
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22: "tho thahan",
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23: "tho thong",
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24: "no nu",
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25: "bo baimai",
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26: "po pla",
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27: "pho phung",
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28: "fo fa",
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29: "pho phan",
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30: "fo fan",
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31: "pho samphao",
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32: "mo ma",
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33: "yo yak",
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34: "ro rua",
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35: "ru",
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36: "lo ling",
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37: "lu",
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+
38: "wo waen",
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39: "so rusi",
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+
40: "so sala",
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+
41: "so sua",
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+
42: "ho hip",
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+
43: "lo chula",
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+
44: "o ang",
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+
45: "ho nokhuk",
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+
46: "paiyannoi",
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+
47: "sara a",
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+
48: "mai han",
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49: "sara aa",
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50: "sara i",
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51: "sara ii",
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52: "sara ue",
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53: "sara uee",
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54: "sara u",
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55: "sara uu",
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56: "sara e",
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57: "sara o",
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58: "sara ai maimuan",
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+
59: "sara ai maimalai",
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60: "maiyamok",
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61: "maitaikhu",
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62: "mai ek",
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63: "mai tho",
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64: "mai tri",
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65: "mai chattawa",
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66: "thanthakhat",
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67: "nikhahit",
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},
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},
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}
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+
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_LICENSE = Licenses.UNKNOWN.value
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+
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_LOCAL = False
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+
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_URLS = {
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_DATASETNAME: "https://www.ai.rug.nl/~mrolarik/ALICE-THI/ALICE-THI-Dataset.tar.gz",
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}
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+
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_SUPPORTED_TASKS = [Tasks.OPTICAL_CHARACTER_RECOGNITION]
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_SEACROWD_SCHEMA = f"seacrowd_{TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()}" # imtext
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_SOURCE_VERSION = "1.0.0"
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+
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_SEACROWD_VERSION = "2024.06.20"
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+
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+
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class AliceTHIDataset(datasets.GeneratorBasedBuilder):
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"""Thai handwritten script dataset for character and digit recognition."""
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+
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
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+
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BUILDER_CONFIGS = []
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for subset in list(_SUBSETS.keys()):
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BUILDER_CONFIGS += [
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SEACrowdConfig(
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name=f"{_DATASETNAME}_{subset}_source",
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version=SOURCE_VERSION,
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description=f"{_DATASETNAME} {subset} source schema",
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schema="source",
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subset_id=subset,
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),
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SEACrowdConfig(
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name=f"{_DATASETNAME}_{subset}_{_SEACROWD_SCHEMA}",
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version=SEACROWD_VERSION,
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description=f"{_DATASETNAME} {subset} SEACrowd schema",
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schema=_SEACROWD_SCHEMA,
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subset_id=subset,
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),
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]
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_THI-C68_source"
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+
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def _info(self) -> datasets.DatasetInfo:
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label_names = [val for _, val in sorted(_SUBSETS[self.config.subset_id]["label_dict"].items())]
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if self.config.schema == "source":
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features = datasets.Features(
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{
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"label": datasets.ClassLabel(names=label_names),
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"text": datasets.Value("string"),
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"image_path": datasets.Value("string"),
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}
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)
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elif self.config.schema == _SEACROWD_SCHEMA:
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features = schemas.image_text_features(label_names=label_names)
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+
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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+
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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"""Returns SplitGenerators."""
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data_name = "ALICE-THI Dataset"
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data_path = Path(dl_manager.download_and_extract(_URLS[_DATASETNAME]))
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data_path = Path(dl_manager.extract(data_path / data_name / f"{data_name}.tar.gz"))
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data_path = data_path / _SUBSETS[self.config.subset_id]["data_dir"]
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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223 |
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"data_path": data_path,
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},
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),
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]
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+
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def _generate_examples(self, data_path: Path) -> Tuple[int, Dict]:
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"""Yields examples as (key, example) tuples."""
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# iterate over files and directories
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231 |
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for subfolder in data_path.iterdir():
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232 |
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if subfolder.is_dir():
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233 |
+
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# source schema yield one image per label
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if self.config.schema == "source":
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_get_label = True # efficiency placeholder
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237 |
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for image_file in subfolder.glob("*.png"):
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if _get_label: # get label from filename
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label = int(image_file.name.split("-")[0].lower())
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_get_label = False
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241 |
+
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yield image_file.stem, {
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"label": label,
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"text": _SUBSETS[self.config.subset_id]["label_dict"][label],
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245 |
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"image_path": str(image_file),
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}
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+
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# seacrowd schema yield multiple images per label
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elif self.config.schema == _SEACROWD_SCHEMA:
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image_files = list(subfolder.glob("*.png"))
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251 |
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label = int(image_files[0].name.split("-")[0].lower())
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252 |
+
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253 |
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yield subfolder.name, {
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"id": subfolder.name,
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+
"image_paths": [str(file) for file in image_files],
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"texts": _SUBSETS[self.config.subset_id]["label_dict"][label],
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+
"metadata": {
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"context": "",
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"labels": [label] * len(image_files),
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+
},
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}
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