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KHATT / KHATT.py

Youssef Benhachem

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	# 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