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