add IIIT-5K dataset

IIIT-5K.py

@@ -0,0 +1,106 @@
+# coding=utf-8
+# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""IIIT5K dataset."""
+
+
+import scipy.io
+
+import datasets
+
+import os
+from pathlib import Path
+
+
+_CITATION = """\
+@InProceedings{MishraBMVC12,
+ author   = "Mishra, A. and Alahari, K. and Jawahar, C.~V.",
+ title    = "Scene Text Recognition using Higher Order Language Priors",
+ booktitle= "BMVC",
+ year     = "2012"
+}
+"""
+
+_DESCRIPTION = """\
+The IIIT 5K-Word dataset is harvested from Google image search. 
+Query words like billboards, signboard, house numbers, house name plates, movie posters were used to collect images. 
+The dataset contains 5000 cropped word images from Scene Texts and born-digital images. 
+The dataset is divided into train and test parts. 
+This dataset can be used for large lexicon cropped word recognition. 
+We also provide a lexicon of more than 0.5 million dictionary words with this dataset.
+"""
+
+_HOMEPAGE = "http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html"
+
+_DL_URL = "http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K-Word_V3.0.tar.gz"
+
+
+class IIIT5K(datasets.GeneratorBasedBuilder):
+    """IIIT-5K dataset."""
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "image": datasets.Image(),
+                "label": datasets.Value("string"),
+                "small_lexicon": datasets.Sequence(datasets.Value("string")),
+                "medium_lexicon": datasets.Sequence(datasets.Value("string")),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download_and_extract(_DL_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "split": "train",
+                    "archive_path": Path(archive_path) / "IIIT5K",
+                    "info_path": Path(archive_path) / "IIIT5K/traindata.mat",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "split": "test",
+                    "archive_path": Path(archive_path) / "IIIT5K",
+                    "info_path": Path(archive_path) / "IIIT5K/testdata.mat",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, split, archive_path, info_path):
+        info = scipy.io.loadmat(info_path)
+        info = info[split+"data"][0]
+
+        for idx, info_ex in enumerate(info):
+            path_image = os.path.join(archive_path, str(info_ex[0][0]))
+            label = str(info_ex[1][0])
+            small_lexicon = [str(lab[0]) for lab in info_ex[2][0]]
+            medium_lexicon = [str(lab[0]) for lab in info_ex[3][0]]
+            yield idx, {
+                "image": path_image,
+                "label": label,
+                "small_lexicon": small_lexicon,
+                "medium_lexicon": medium_lexicon,
+            }