Create KHATT.py

KHATT.py

@@ -0,0 +1,103 @@
+# coding=utf-8
+# Copyright 2022 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.
+
+import datasets
+
+
+_CITATION = """\
+@article{Pattern Recognition,
+    Author = {bri A. Mahmoud, Irfan Ahmad, Wasfi G. Al-Khatib, Mohammad Alshayeb, Mohammad Tanvir Parvez, Volker Märgner, Gernot A. Fink},
+    Title = { {KHATT: An Open Arabic Offline Handwritten Text Database} },
+    Year = {2013},
+    doi = {10.1016/j.patcog.2013.08.009},
+}
+"""
+
+_HOMEPAGE = "https://khatt.ideas2serve.net/KHATTAgreement.php"
+
+_DESCRIPTION = """\
+KHATT (KFUPM Handwritten Arabic TexT) database is a database of unconstrained handwritten Arabic Text written by 1000 different writers. This research database’s development was undertaken by a research group from KFUPM, Dhahran, S audi Arabia headed by Professor Sabri Mahmoud in collaboration with Professor Fink from TU-Dortmund, Germany and Dr. Märgner from TU-Braunschweig, Germany.
+"""
+
+_DATA_URL = {
+    "train": [
+        "https://huggingface.co/datasets/benhachem/KHATT/resolve/main/data/train.zip"
+    ],
+    "validation": [
+        "https://huggingface.co/datasets/benhachem/KHATT/resolve/main/data/validation.zip"
+    ],
+
+}
+
+
+class KHATT(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "text": datasets.Value("string"),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        archives = dl_manager.download(_DATA_URL)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "archives": [dl_manager.iter_archive(archive) for archive in archives["train"]],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "archives": [dl_manager.iter_archive(archive) for archive in archives["validation"]],
+                    "split": "validation",
+                },
+            ),
+        ]
+    def _generate_examples(self, archives, split):
+        """Yields examples."""
+        idx = 0
+
+        for archive in archives:
+            for path, file in archive:
+                # If we have an image
+                if path.endswith(".tif"):
+                    if split != "test":
+                        img_file = file                          
+                    else:
+                        text = ""
+
+                elif path.endswith(".txt"):
+
+                    text = file.read()
+                    text = text.decode('utf-8')
+
+                    ex = {"image": {"path": path, "bytes": img_file.read()}, "text": text}
+
+                    yield idx, ex
+
+                    idx += 1