''' Reference: https://huggingface.co/datasets/pierresi/cord/blob/main/cord.py ''' import json import os from pathlib import Path 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 = "https://github.com/clovaai/cord/" _URLS = [ "https://hcsun.net/assets/files/CORD-1k-001.zip", "https://hcsun.net/assets/files/CORD-1k-002.zip" ] class Cord(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ datasets.BuilderConfig( name="cord", version=datasets.Version("1.0.0"), description="CORD dataset" ), ] def _info(self): 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"))), "ner_tags": datasets.Sequence( datasets.features.ClassLabel( names=["O","B-MENU.NM","B-MENU.NUM","B-MENU.UNITPRICE","B-MENU.CNT","B-MENU.DISCOUNTPRICE","B-MENU.PRICE","B-MENU.ITEMSUBTOTAL","B-MENU.VATYN","B-MENU.ETC","B-MENU.SUB_NM","B-MENU.SUB_UNITPRICE","B-MENU.SUB_CNT","B-MENU.SUB_PRICE","B-MENU.SUB_ETC","B-VOID_MENU.NM","B-VOID_MENU.PRICE","B-SUB_TOTAL.SUBTOTAL_PRICE","B-SUB_TOTAL.DISCOUNT_PRICE","B-SUB_TOTAL.SERVICE_PRICE","B-SUB_TOTAL.OTHERSVC_PRICE","B-SUB_TOTAL.TAX_PRICE","B-SUB_TOTAL.ETC","B-TOTAL.TOTAL_PRICE","B-TOTAL.TOTAL_ETC","B-TOTAL.CASHPRICE","B-TOTAL.CHANGEPRICE","B-TOTAL.CREDITCARDPRICE","B-TOTAL.EMONEYPRICE","B-TOTAL.MENUTYPE_CNT","B-TOTAL.MENUQTY_CNT","I-MENU.NM","I-MENU.NUM","I-MENU.UNITPRICE","I-MENU.CNT","I-MENU.DISCOUNTPRICE","I-MENU.PRICE","I-MENU.ITEMSUBTOTAL","I-MENU.VATYN","I-MENU.ETC","I-MENU.SUB_NM","I-MENU.SUB_UNITPRICE","I-MENU.SUB_CNT","I-MENU.SUB_PRICE","I-MENU.SUB_ETC","I-VOID_MENU.NM","I-VOID_MENU.PRICE","I-SUB_TOTAL.SUBTOTAL_PRICE","I-SUB_TOTAL.DISCOUNT_PRICE","I-SUB_TOTAL.SERVICE_PRICE","I-SUB_TOTAL.OTHERSVC_PRICE","I-SUB_TOTAL.TAX_PRICE","I-SUB_TOTAL.ETC","I-TOTAL.TOTAL_PRICE","I-TOTAL.TOTAL_ETC","I-TOTAL.CASHPRICE","I-TOTAL.CHANGEPRICE","I-TOTAL.CREDITCARDPRICE","I-TOTAL.EMONEYPRICE","I-TOTAL.MENUTYPE_CNT","I-TOTAL.MENUQTY_CNT"] ) ), "image": datasets.features.Image(), } ), supervised_keys=None, citation=_CITATION, homepage="https://github.com/clovaai/cord/", ) def _split_generators(self, dl_manager): downloaded_file = dl_manager.download_and_extract(_URLS) dest = Path(downloaded_file[0])/"CORD" self._move_files_to_dest(downloaded_file, dest) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepath": dest/"train"} ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"filepath": dest/"dev"} ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"filepath": dest/"test"} ), ] def _move_files_to_dest(self, downloaded_file, dest): for split in ["train", "dev", "test"]: for file_type in ["image", "json"]: if split == "test" and file_type == "json": continue files = (Path(downloaded_file[1])/"CORD"/split/file_type).iterdir() for f in files: os.rename(f, dest/split/file_type/f.name) def _generate_examples(self, 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))): file_path = os.path.join(ann_dir, file) with open(file_path, "r", encoding="utf8") as f: data = json.load(f) image_path = os.path.join(img_dir, file).replace("json", "png") image, size = self._load_image(image_path) words, bboxes, ner_tags = self._process_data(data, size) yield guid, {"id": str(guid), "words": words, "bboxes": bboxes, "ner_tags": ner_tags, "image": image} def _load_image(self, image_path): image = Image.open(image_path).convert("RGB") w, h = image.size return image, (w, h) def _process_data(self, data, size): words = [] bboxes = [] ner_tags = [] for item in data["valid_line"]: line_words, label = item["words"], item["category"] line_words = [w for w in line_words if w["text"].strip() != ""] if len(line_words) == 0: continue cur_line_bboxes = self._process_line_words(line_words, label, size, words, ner_tags) bboxes.extend(cur_line_bboxes) return words, bboxes, ner_tags def _process_line_words(self, line_words, label, size, words, ner_tags): cur_line_bboxes = [] if label == "other": for w in line_words: words.append(w["text"]) ner_tags.append("O") cur_line_bboxes.append(self._normalize_bbox(self._quad_to_box(w["quad"]), size)) else: words.append(line_words[0]["text"]) ner_tags.append("B-" + label.upper()) cur_line_bboxes.append(self._normalize_bbox(self._quad_to_box(line_words[0]["quad"]), size)) for w in line_words[1:]: words.append(w["text"]) ner_tags.append("I-" + label.upper()) cur_line_bboxes.append(self._normalize_bbox(self._quad_to_box(w["quad"]), size)) cur_line_bboxes = self._get_line_bbox(cur_line_bboxes) return cur_line_bboxes def _normalize_bbox(self, bbox, size): return [ int(1000 * bbox[0] / size[0]), int(1000 * bbox[1] / size[1]), int(1000 * bbox[2] / size[0]), int(1000 * bbox[3] / size[1]), ] def _quad_to_box(self, quad): box = ( max(0, quad["x1"]), max(0, quad["y1"]), quad["x3"], quad["y3"] ) if box[3] < box[1] or box[2] < box[0]: box = self._fix_box(box) return box def _fix_box(self, box): bbox = list(box) if box[3] < box[1]: bbox[1], bbox[3] = bbox[3], bbox[1] if box[2] < box[0]: bbox[0], bbox[2] = bbox[2], bbox[0] return tuple(bbox) def _get_line_bbox(self, bboxs): x = [bboxs[i][j] for i in range(len(bboxs)) for j in range(0, len(bboxs[i]), 2)] y = [bboxs[i][j] for i in range(len(bboxs)) for j in range(1, len(bboxs[i]), 2)] x0, y0, x1, y1 = min(x), min(y), max(x), max(y) assert x1 >= x0 and y1 >= y0 return [[x0, y0, x1, y1] for _ in range(len(bboxs))]