Init v1.0.0

dataset_infos.json

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+{"CORD": {"description": "CORD (Consolidated Receipt Dataset) with normalized bounding boxes.\n", "citation": "@article{park2019cord,\n  title={CORD: A Consolidated Receipt Dataset for Post-OCR Parsing},\n  author={Park, Seunghyun and Shin, Seung and Lee, Bado and Lee, Junyeop and Surh, Jaeheung and Seo, Minjoon and Lee, Hwalsuk}\n  booktitle={Document Intelligence Workshop at Neural Information Processing Systems}\n  year={2019}\n}\n", "homepage": "https://github.com/clovaai/cord/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "words": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "bboxes": {"feature": {"feature": {"dtype": "int64", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "labels": {"feature": {"num_classes": 30, "names": ["menu.cnt", "menu.discountprice", "menu.etc", "menu.itemsubtotal", "menu.nm", "menu.num", "menu.price", "menu.sub_cnt", "menu.sub_etc", "menu.sub_nm", "menu.sub_price", "menu.sub_unitprice", "menu.unitprice", "menu.vatyn", "sub_total.discount_price", "sub_total.etc", "sub_total.othersvc_price", "sub_total.service_price", "sub_total.subtotal_price", "sub_total.tax_price", "total.cashprice", "total.changeprice", "total.creditcardprice", "total.emoneyprice", "total.menuqty_cnt", "total.menutype_cnt", "total.total_etc", "total.total_price", "void_menu.nm", "void_menu.price"], "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}, "images": {"decode": true, "id": null, "_type": "Image"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "cord", "config_name": "CORD", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1296352297, "num_examples": 800, "dataset_name": "cord"}, "validation": {"name": "validation", "num_bytes": 171508384, "num_examples": 100, "dataset_name": "cord"}, "test": {"name": "test", "num_bytes": 163511722, "num_examples": 100, "dataset_name": "cord"}}, "download_checksums": {"https://drive.google.com/uc?id=1MqhTbcj-AHXOqYoeoh12aRUwIprzTJYI": {"num_bytes": 2125977855, "checksum": "75ba4dcff40422d4a9404081553ae70f3c1d6939f67dc56fcccae4044b6ef027"}, "https://drive.google.com/uc?id=1wYdp5nC9LnHQZ2FcmOoC0eClyWvcuARU": {"num_bytes": 187263122, "checksum": "bcad043c415fe14302657d784102cc7f4c47ba34a92eec96b6f600a6a9dd9764"}}, "download_size": 2313240977, "post_processing_size": null, "dataset_size": 1631372403, "size_in_bytes": 3944613380}}

load_script.py

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+"""CORD: A Consolidated Receipt Dataset for Post-OCR Parsing"""
+
+
+import json
+import os
+from pathlib import Path
+from typing import Any, Generator
+
+import datasets
+from PIL import Image
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@article{park2019cord,
+  title={CORD: A Consolidated Receipt Dataset for Post-OCR Parsing},
+  author={Park, Seunghyun and Shin, Seung and Lee, Bado and Lee, Junyeop and Surh, Jaeheung and Seo, Minjoon and Lee, Hwalsuk}
+  booktitle={Document Intelligence Workshop at Neural Information Processing Systems}
+  year={2019}
+}
+"""
+
+_DESCRIPTION = """\
+CORD (Consolidated Receipt Dataset) with normalized bounding boxes.
+"""
+
+_URLS = [
+    "https://drive.google.com/uc?id=1MqhTbcj-AHXOqYoeoh12aRUwIprzTJYI",
+    "https://drive.google.com/uc?id=1wYdp5nC9LnHQZ2FcmOoC0eClyWvcuARU",
+]
+
+_LABELS = [
+    "menu.cnt",
+    "menu.discountprice",
+    "menu.etc",
+    "menu.itemsubtotal",
+    "menu.nm",
+    "menu.num",
+    "menu.price",
+    "menu.sub_cnt",
+    "menu.sub_etc",
+    "menu.sub_nm",
+    "menu.sub_price",
+    "menu.sub_unitprice",
+    "menu.unitprice",
+    "menu.vatyn",
+    "sub_total.discount_price",
+    "sub_total.etc",
+    "sub_total.othersvc_price",
+    "sub_total.service_price",
+    "sub_total.subtotal_price",
+    "sub_total.tax_price",
+    "total.cashprice",
+    "total.changeprice",
+    "total.creditcardprice",
+    "total.emoneyprice",
+    "total.menuqty_cnt",
+    "total.menutype_cnt",
+    "total.total_etc",
+    "total.total_price",
+    "void_menu.nm",
+    "void_menu.price",
+]
+
+
+def load_image(image_path: str) -> tuple:
+    image = Image.open(image_path).convert("RGB")
+    return image, image.size
+
+
+def quad_to_bbox(quad: dict) -> list:
+    return [
+        quad["x3"],
+        quad["y1"],
+        quad["x1"],
+        quad["y3"],
+    ]
+
+
+def normalize_bbox(bbox: list, width: int, height: int) -> list:
+    return [
+        int(1000 * (bbox[0] / width)),
+        int(1000 * (bbox[1] / height)),
+        int(1000 * (bbox[2] / width)),
+        int(1000 * (bbox[3] / height)),
+    ]
+
+
+class CORDConfig(datasets.BuilderConfig):
+    """BuilderConfig for CORD."""
+
+    def __init__(self, **kwargs) -> None:
+        """BuilderConfig for CORD.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(CORDConfig, self).__init__(**kwargs)
+
+
+class CORD(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        CORDConfig(
+            name="CORD",
+            version=datasets.Version("1.0.0"),
+            description="CORD (Consolidated Receipt Dataset)",
+        ),
+    ]
+
+    def _info(self) -> datasets.DatasetInfo:
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "words": datasets.Sequence(datasets.Value("string")),
+                    "bboxes": datasets.Sequence(
+                        datasets.Sequence(datasets.Value("int64"))
+                    ),
+                    "labels": datasets.Sequence(
+                        datasets.features.ClassLabel(names=_LABELS)
+                    ),
+                    "images": datasets.features.Image(),
+                }
+            ),
+            citation=_CITATION,
+            homepage="https://github.com/clovaai/cord/",
+        )
+
+    def _split_generators(self, dl_manager) -> list:
+        base_dir_v1, base_dir_v2 = dl_manager.download_and_extract(_URLS)
+        dest_dir = Path(base_dir_v1) / "CORD"
+
+        for split_dir in ["train", "dev", "test"]:
+            for type_dir in ["image", "json"]:
+                if split_dir == "test" and type_dir == "json":
+                    continue
+                files = (Path(base_dir_v2) / "CORD" / split_dir / type_dir).iterdir()
+                for f in files:
+                    os.rename(f, dest_dir / split_dir / type_dir / f.name)
+
+        return [
+            datasets.SplitGenerator(
+                name=str(datasets.Split.TRAIN), gen_kwargs={"filepath": dest_dir / "train"}
+            ),
+            datasets.SplitGenerator(
+                name=str(datasets.Split.VALIDATION), gen_kwargs={"filepath": dest_dir / "dev"},
+            ),
+            datasets.SplitGenerator(
+                name=str(datasets.Split.TEST), gen_kwargs={"filepath": dest_dir / "test"}
+            ),
+        ]
+
+    def _generate_examples(self, **kwargs: Any) -> Generator:
+        filepath = kwargs["filepath"]
+        logger.info("generating examples from = %s", filepath)
+        ann_dir = os.path.join(filepath, "json")
+        img_dir = os.path.join(filepath, "image")
+
+        for guid, file in enumerate(sorted(os.listdir(ann_dir))):
+            WORDS, BBOXES, LABELS = [], [], []
+            file_path = os.path.join(ann_dir, file)
+            f = open(file_path)
+            data = json.load(f)
+
+            image_path = os.path.join(img_dir, file).replace("json", "png")
+            image, (width, height) = load_image(image_path)
+
+            for annotation in data["valid_line"]:
+                label, words = annotation["category"], annotation["words"]
+                for word in words:
+                    bbox = normalize_bbox(
+                        quad_to_bbox(word["quad"]), width=width, height=height
+                    )
+                    if min(bbox) >= 0 and max(bbox) <= 1000:
+                        WORDS.append(word["text"])
+                        BBOXES.append(bbox)
+                        LABELS.append(label)
+
+            yield guid, {
+                "id": str(guid),
+                "images": image,
+                "words": WORDS,
+                "bboxes": BBOXES,
+                "labels": LABELS,
+            }