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from xml.etree import ElementTree as ET |
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import datasets |
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_CITATION = """\ |
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@InProceedings{huggingface:dataset, |
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title = {ocr-receipts-text-detection}, |
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author = {TrainingDataPro}, |
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year = {2023} |
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} |
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""" |
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_DESCRIPTION = """\ |
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The Grocery Store Receipts Dataset is a collection of photos captured from various |
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**grocery store receipts**. This dataset is specifically designed for tasks related to |
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**Optical Character Recognition (OCR)** and is useful for retail. |
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Each image in the dataset is accompanied by bounding box annotations, indicating the |
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precise locations of specific text segments on the receipts. The text segments are |
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categorized into four classes: **item, store, date_time and total**. |
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""" |
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_NAME = "ocr-receipts-text-detection" |
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_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" |
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_LICENSE = "" |
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_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" |
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_LABELS = ["receipt", "shop", "item", "date_time", "total"] |
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class OcrReceiptsTextDetection(datasets.GeneratorBasedBuilder): |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("int32"), |
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"name": datasets.Value("string"), |
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"image": datasets.Image(), |
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"mask": datasets.Image(), |
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"width": datasets.Value("uint16"), |
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"height": datasets.Value("uint16"), |
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"shapes": datasets.Sequence( |
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{ |
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"label": datasets.ClassLabel( |
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num_classes=len(_LABELS), |
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names=_LABELS, |
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), |
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"type": datasets.Value("string"), |
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"points": datasets.Sequence( |
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datasets.Sequence( |
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datasets.Value("float"), |
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), |
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), |
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"rotation": datasets.Value("float"), |
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"occluded": datasets.Value("uint8"), |
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"attributes": datasets.Sequence( |
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{ |
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"name": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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} |
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), |
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} |
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), |
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} |
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), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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images = dl_manager.download(f"{_DATA}images.tar.gz") |
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masks = dl_manager.download(f"{_DATA}boxes.tar.gz") |
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annotations = dl_manager.download(f"{_DATA}annotations.xml") |
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images = dl_manager.iter_archive(images) |
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masks = dl_manager.iter_archive(masks) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"images": images, |
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"masks": masks, |
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"annotations": annotations, |
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}, |
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), |
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] |
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@staticmethod |
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def parse_shape(shape: ET.Element) -> dict: |
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label = shape.get("label") |
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shape_type = shape.tag |
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rotation = shape.get("rotation", 0.0) |
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occluded = shape.get("occluded", 0) |
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points = None |
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if shape_type == "points": |
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points = tuple(map(float, shape.get("points").split(","))) |
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elif shape_type == "box": |
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points = [ |
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(float(shape.get("xtl")), float(shape.get("ytl"))), |
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(float(shape.get("xbr")), float(shape.get("ybr"))), |
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] |
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elif shape_type == "polygon": |
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points = [ |
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tuple(map(float, point.split(","))) |
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for point in shape.get("points").split(";") |
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] |
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attributes = [] |
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for attr in shape: |
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attr_name = attr.get("name") |
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attr_text = attr.text |
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attributes.append({"name": attr_name, "text": attr_text}) |
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shape_data = { |
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"label": label, |
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"type": shape_type, |
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"points": points, |
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"rotation": rotation, |
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"occluded": occluded, |
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"attributes": attributes, |
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} |
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return shape_data |
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def _generate_examples(self, images, masks, annotations): |
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tree = ET.parse(annotations) |
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root = tree.getroot() |
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for idx, ( |
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(image_path, image), |
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(mask_path, mask), |
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) in enumerate(zip(images, masks)): |
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image_name = image_path.split("/")[-1] |
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img = root.find(f"./image[@name='images/{image_name}']") |
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image_id = img.get("id") |
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name = img.get("name") |
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width = img.get("width") |
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height = img.get("height") |
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shapes = [self.parse_shape(shape) for shape in img] |
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yield idx, { |
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"id": image_id, |
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"name": name, |
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"image": {"path": image_path, "bytes": image.read()}, |
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"mask": {"path": mask_path, "bytes": mask.read()}, |
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"width": width, |
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"height": height, |
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"shapes": shapes, |
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} |
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