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burapha_th.py

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+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@Article{app12084083,
+AUTHOR = {Onuean, Athita and Buatoom, Uraiwan and Charoenporn, Thatsanee and Kim, Taehong and Jung, Hanmin},
+TITLE = {Burapha-TH: A Multi-Purpose Character, Digit, and Syllable Handwriting Dataset},
+JOURNAL = {Applied Sciences},
+VOLUME = {12},
+YEAR = {2022},
+NUMBER = {8},
+ARTICLE-NUMBER = {4083},
+URL = {https://www.mdpi.com/2076-3417/12/8/4083},
+ISSN = {2076-3417},
+DOI = {10.3390/app12084083}
+}
+"""
+_DATASETNAME = "burapha_th"
+
+_DESCRIPTION = """\
+The dataset has 68 character classes, 10 digit classes, and 320 syllable classes.
+For constructing the dataset, 1072 Thai native speakers wrote on collection datasheets
+that were then digitized using a 300 dpi scanner.
+De-skewing, detection box and segmentation algorithms were applied to the raw scans
+for image extraction. The dataset, unlike all other known Thai handwriting datasets, retains
+existing noise, the white background, and all artifacts generated by scanning.
+"""
+
+_HOMEPAGE = "https://services.informatics.buu.ac.th/datasets/Burapha-TH/"
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LOCAL = False
+_LANGUAGES = ["tha"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+
+_URLS = {
+    "character": {"test": "https://services.informatics.buu.ac.th/datasets/Burapha-TH/character/20210306-test.zip", "train": "https://services.informatics.buu.ac.th/datasets/Burapha-TH/character/20210306-train.zip"},
+    "digit": {"test": "https://services.informatics.buu.ac.th/datasets/Burapha-TH/digit/20210307-test.zip", "train": "https://services.informatics.buu.ac.th/datasets/Burapha-TH/digit/20210307-train.zip"},
+    "syllable": {"test": "https://services.informatics.buu.ac.th/datasets/Burapha-TH/syllable/20210309-test-ori.zip", "train": "https://services.informatics.buu.ac.th/datasets/Burapha-TH/syllable/20210309-train-ori.zip"},
+}
+
+_SUPPORTED_TASKS = [Tasks.IMAGE_CAPTIONING]
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+_SUBSETS = ["character", "digit", "syllable"]
+
+
+def config_constructor(subset: str, schema: str, version: str) -> SEACrowdConfig:
+    return SEACrowdConfig(
+        name=f"{_DATASETNAME}_{subset}_{schema}",
+        version=version,
+        description=f"{_DATASETNAME} {subset} {schema} schema",
+        schema=f"{schema}",
+        subset_id=f"{_DATASETNAME}_{subset}",
+    )
+
+
+class BuraphaThDataset(datasets.GeneratorBasedBuilder):
+    """
+    The dataset has 68 character classes, 10 digit classes, and 320 syllable classes.
+    For constructing the dataset, 1072 Thai native speakers wrote on collection datasheets
+    that were then digitized using a 300 dpi scanner.
+    De-skewing, detection box and segmentation algorithms were applied to the raw scans for
+    image extraction. The dataset, unlike all other known Thai handwriting datasets, retains
+    existing noise, the white background, and all artifacts generated by scanning.
+    """
+
+    BUILDER_CONFIGS = [config_constructor(subset, "source", _SOURCE_VERSION) for subset in _SUBSETS]
+    BUILDER_CONFIGS.extend([config_constructor(subset, "seacrowd_imtext", _SEACROWD_VERSION) for subset in _SUBSETS])
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_digit_source"
+
+    label_chr_dig = [str(i).zfill(2) for i in range(78)]
+    label_syl = [str(i).zfill(3) for i in range(320)]
+
+    def _info(self) -> datasets.DatasetInfo:
+        task = self.config.subset_id.split("_")[2]
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {"id": datasets.Value("string"), "image_paths": datasets.Value("string"), "label": datasets.Sequence(datasets.ClassLabel(names=self.label_chr_dig if task == "character" or task == "digit" else self.label_syl))}
+            )
+        elif self.config.schema == "seacrowd_imtext":
+            features = schemas.image_text_features(label_names=self.label_chr_dig if task == "character" or task == "digit" else self.label_syl)
+        else:
+            raise NotImplementedError()
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        task = self.config.subset_id.split("_")[2]
+
+        _local_path = dl_manager.download_and_extract(_URLS[task])
+        train_path, test_path = _local_path["train"], _local_path["test"]
+        if task in ["character", "digit"]:
+            train_path = os.path.join(train_path, "train")
+            test_path = os.path.join(test_path, "test")
+        # for "syllable" type task
+        else:
+            train_path = os.path.join(train_path, "train-ori")
+            test_path = os.path.join(test_path, "test-ori")
+
+        data_pair = {}
+
+        for dir_name in os.listdir(train_path):
+            dir_name_split = dir_name.split("-")
+            file_names = []
+
+            for file_name in os.listdir(os.path.join(train_path, dir_name)):
+                file_names.append(os.path.join(train_path, dir_name, file_name))
+
+            label = dir_name_split[0]
+            data_pair[label] = file_names
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_pair,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_pair,
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        task = self.config.subset_id.split("_")[2]
+        counter = 0
+
+        for key, imgs in filepath.items():
+            for img in imgs:
+                if self.config.schema == "source":
+                    yield counter, {"id": str(counter), "image_paths": img, "label": [self.label_chr_dig.index(key) if task == "character" or task == "digit" else self.label_syl.index(key)]}
+                elif self.config.schema == "seacrowd_imtext":
+                    yield counter, {
+                        "id": str(counter),
+                        "image_paths": [img],
+                        "texts": None,
+                        "metadata": {
+                            "context": None,
+                            "labels": [self.label_chr_dig.index(key) if task in ["character", "digit"] else self.label_syl.index(key)],
+                        },
+                    }
+                counter += 1