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"""IIIT5K dataset.""" |
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import scipy.io |
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import datasets |
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import os |
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from pathlib import Path |
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_CITATION = """\ |
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@InProceedings{MishraBMVC12, |
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author = "Mishra, A. and Alahari, K. and Jawahar, C.~V.", |
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title = "Scene Text Recognition using Higher Order Language Priors", |
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booktitle= "BMVC", |
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year = "2012" |
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} |
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""" |
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_DESCRIPTION = """\ |
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The IIIT 5K-Word dataset is harvested from Google image search. |
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Query words like billboards, signboard, house numbers, house name plates, movie posters were used to collect images. |
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The dataset contains 5000 cropped word images from Scene Texts and born-digital images. |
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The dataset is divided into train and test parts. |
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This dataset can be used for large lexicon cropped word recognition. |
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We also provide a lexicon of more than 0.5 million dictionary words with this dataset. |
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""" |
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_HOMEPAGE = "http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html" |
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_DL_URL = "http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K-Word_V3.0.tar.gz" |
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class IIIT5K(datasets.GeneratorBasedBuilder): |
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"""IIIT-5K dataset.""" |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"label": datasets.Value("string"), |
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"small_lexicon": datasets.Sequence(datasets.Value("string")), |
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"medium_lexicon": datasets.Sequence(datasets.Value("string")), |
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} |
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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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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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archive_path = dl_manager.download_and_extract(_DL_URL) |
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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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"split": "train", |
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"archive_path": Path(archive_path) / "IIIT5K", |
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"info_path": Path(archive_path) / "IIIT5K/traindata.mat", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"split": "test", |
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"archive_path": Path(archive_path) / "IIIT5K", |
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"info_path": Path(archive_path) / "IIIT5K/testdata.mat", |
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}, |
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), |
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] |
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def _generate_examples(self, split, archive_path, info_path): |
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info = scipy.io.loadmat(info_path) |
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info = info[split+"data"][0] |
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for idx, info_ex in enumerate(info): |
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path_image = os.path.join(archive_path, str(info_ex[0][0])) |
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label = str(info_ex[1][0]) |
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small_lexicon = [str(lab[0]) for lab in info_ex[2][0]] |
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medium_lexicon = [str(lab[0]) for lab in info_ex[3][0]] |
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yield idx, { |
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"image": path_image, |
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"label": label, |
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"small_lexicon": small_lexicon, |
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"medium_lexicon": medium_lexicon, |
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} |
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