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Upload kvis_th_ocr.py with huggingface_hub
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kvis_th_ocr.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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import os
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from pathlib import Path
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from typing import Dict, List, Tuple
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import datasets
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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 Licenses, Tasks
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_CITATION = """\
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@INPROCEEDINGS{8584876,
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author={Joseph, Ferdin Joe John and Anantaprayoon, Panatchakorn},
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booktitle={2018 International Conference on Information Technology (InCIT)},
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title={Offline Handwritten Thai Character Recognition Using Single Tier Classifier and Local Features},
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year={2018},
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volume={},
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number={},
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pages={1-4},
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abstract={Handwritten character recognition is a conversion process of handwriting into machine-encoded text. Currently,
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several techniques and methods are proposed to enhance accuracy of handwritten character recognition for many languages
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spoken across the globe. In this project, a local feature-based approach is proposed to enhance the accuracy of handwritten
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offline character recognition for Thai alphabets. In the experiment, through MATLAB, 100 images for each class of Thai
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alphabets are collected and k-fold cross validation is applied to manage datasets to train and test. A gradient invariant
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feature set consisting of LBP and shape features is extracted. The classification is operated by using query matching based
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on Euclidean distance. The accuracy would be the percentage of correct classification for each class. For the result, the
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highest accuracy is 68.96% which has 144-bit shape features and uniform pattern LBP for the features.},
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keywords={Character recognition;Feature extraction;Shape;Genetic algorithms;Matlab;Gray-scale;Optical character recognition
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software;Offline Character Recognition;Local Binary Pattern;Thai Handwriting},
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doi={10.23919/INCIT.2018.8584876},
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ISSN={},
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month={Oct},
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url={https://ieeexplore.ieee.org/document/8584876}}
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"""
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+
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_DATASETNAME = "kvis_th_ocr"
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+
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_DESCRIPTION = """\
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The KVIS Thai OCR Dataset contains scanned handwritten version of all 44 Thai characters obtained from 27 individuals. It
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consisted of 1079 images from 44 classes (letters). This dataset consists of all Thai consonants with different writing
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styles of various people from ages between 16 and 75. Vowels and intonation are not taken into consideration for the dataset
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collected.
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"""
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_HOMEPAGE = "https://data.mendeley.com/datasets/8nr3pbdk5c/1"
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_LANGUAGES = ["tha"]
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_LICENSE = Licenses.CC_BY_4_0.value
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_LOCAL = False
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_URLS = "https://prod-dcd-datasets-cache-zipfiles.s3.eu-west-1.amazonaws.com/8nr3pbdk5c-1.zip"
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_SUPPORTED_TASKS = [Tasks.OPTICAL_CHARACTER_RECOGNITION]
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_SOURCE_VERSION = "1.0.0"
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_SEACROWD_VERSION = "2024.06.20"
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class KVISThaiOCRDataset(datasets.GeneratorBasedBuilder):
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"""
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KVIS Thai OCR is a dataset for optical character recognition for Thai characters from https://data.mendeley.com/datasets/8nr3pbdk5c/1.
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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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labels = ["ก", "ข", "ฃ", "ค", "ฅ", "ฆ", "ง", "จ", "ฉ", "ช", "ซ", "ฌ", "ญ", "ฎ", "ฏ", "ฐ", "ฑ", "ฒ", "ณ", "ด", "ต", "ถ", "ท", "ธ", "น", "บ", "ป", "ผ", "ฝ", "พ", "ฟ", "ภ", "ม", "ย", "ร", "ล", "ว", "ศ", "ษ", "ส", "ห", "ฬ", "อ", "ฮ"]
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BUILDER_CONFIGS = [
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SEACrowdConfig(
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name=f"{_DATASETNAME}_source",
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version=datasets.Version(_SOURCE_VERSION),
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description=f"{_DATASETNAME} source schema",
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schema="source",
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subset_id=f"{_DATASETNAME}",
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),
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SEACrowdConfig(
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name=f"{_DATASETNAME}_seacrowd_imtext",
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version=datasets.Version(_SOURCE_VERSION),
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description=f"{_DATASETNAME} SEACrowd schema",
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schema="seacrowd_imtext",
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subset_id=f"{_DATASETNAME}",
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),
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]
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def _info(self) -> datasets.DatasetInfo:
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if self.config.schema == "source":
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features = datasets.Features(
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{
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"id": datasets.Value("string"),
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"file_path": datasets.Value("string"),
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"character": datasets.Value("string"),
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}
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)
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elif self.config.schema == "seacrowd_imtext":
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features = schemas.image_text_features(label_names=self.labels)
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else:
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raise ValueError(f"Invalid schema: {self.config.schema}")
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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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"""
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Returns SplitGenerators.
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"""
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dir = dl_manager.download_and_extract(_URLS)
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path = dl_manager.extract(os.path.join(dir, "KVIS TOCR Dataset.zip"))
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+
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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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"path": path,
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"split": "train",
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},
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),
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]
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+
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def _generate_examples(self, path: Path, split: str) -> Tuple[int, Dict]:
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"""
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Yields examples as (key, example) tuples.
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"""
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+
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idx = 0
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path = list(os.walk(path))
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for directory in path[1:]:
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label = directory[0][-3:-2]
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for file in directory[2]:
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file_extension = str(file[-3:])
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if file_extension == "jpg":
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file_id = str(file[:-4])
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file_path = os.path.join(directory[0], file)
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if self.config.schema == "source":
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data = {
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"id": file_id,
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"file_path": file_path,
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"character": label,
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}
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yield idx, data
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idx += 1
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elif self.config.schema == "seacrowd_imtext":
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data = {
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"id": file_id,
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"image_paths": [file_path],
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"texts": "",
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"metadata": {
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+
"context": "",
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"labels": [label],
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},
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}
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yield idx, data
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idx += 1
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else:
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raise ValueError(f"Invalid schema: {self.config.schema}")
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