KHATT.py

# 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