Datasets:
File size: 3,583 Bytes
81100fc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 |
# 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
|