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"""Medical Prescription OCR Dataset"""
import json
import os
import datasets
from PIL import Image
_DESCRIPTION = """
Medical Prescription OCR Dataset - A collection of synthetic handwritten medical prescriptions
with structured annotations for training OCR models.
"""
_CITATION = """
@dataset{shrivastava2024medicalprescription,
author = {Chinmay Shrivastava},
title = {Medical Prescription OCR Dataset},
year = {2024},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/chinmays18/medical-prescription-dataset}
}
"""
class MedicalPrescriptionDataset(datasets.GeneratorBasedBuilder):
"""Medical Prescription OCR Dataset"""
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({
"image": datasets.Image(),
"ground_truth": datasets.Value("string"),
}),
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# The dataset files are already in the repository
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"images_path": "train/images",
"annotations_path": "train/annotations",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"images_path": "val/images",
"annotations_path": "val/annotations",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"images_path": "test/images",
"annotations_path": "test/annotations",
},
),
]
def _generate_examples(self, images_path, annotations_path):
# Get all image files
image_files = sorted([f for f in os.listdir(images_path) if f.endswith('.png')])
for idx, image_file in enumerate(image_files):
# Get corresponding annotation file
base_name = os.path.splitext(image_file)[0]
annotation_file = f"{base_name}.json"
# Read image
image_path = os.path.join(images_path, image_file)
# Read annotation
annotation_path = os.path.join(annotations_path, annotation_file)
with open(annotation_path, 'r') as f:
annotation = json.load(f)
yield idx, {
"image": image_path,
"ground_truth": annotation["ground_truth"],
}
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