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import json
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import os
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import csv
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import re
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import datasets
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from PIL import Image
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import numpy as np
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logger = datasets.logging.get_logger(__name__)
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_CITATION = """\
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@inproceedings{yu2021pick,
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title={PICK: Processing key information extraction from documents using improved graph learning-convolutional networks},
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author={Yu, Wenwen and Lu, Ning and Qi, Xianbiao and Gong, Ping and Xiao, Rong},
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booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},
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pages={4363--4370},
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year={2021},
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organization={IEEE}
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}
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"""
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_DESCRIPTION = """\
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The train ticket is fixed layout dataset, however, it contains background noise and imaging distortions.
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It contains 1,530 synthetic images and 320 real images for training, and 80 real images for testing.
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Every train ticket has eight key text fields including ticket number, starting station, train number, destination station, date, ticket rates, seat category, and name.
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This dataset mainly consists of digits, English characters, and Chinese characters.
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"""
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_URL = """\
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https://drive.google.com/file/d/1o8JktPD7bS74tfjz-8dVcZq_uFS6YEGh/view?usp=sharing
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"""
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def load_image(image_path):
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image = Image.open(image_path).convert("RGB")
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w, h = image.size
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return image, (w, h)
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def normalize_bbox(bbox, size):
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return [
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int(1000 * bbox[0] / size[0]),
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int(1000 * bbox[1] / size[1]),
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int(1000 * bbox[2] / size[0]),
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int(1000 * bbox[3] / size[1]),
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]
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class TrainTicketsConfig(datasets.BuilderConfig):
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"""BuilderConfig for train_tickets"""
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def __init__(self, **kwargs):
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"""BuilderConfig for train_tickets.
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Args:
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**kwargs: keyword arguments forwarded to super.
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"""
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super(TrainTicketsConfig, self).__init__(**kwargs)
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class TrainTickets(datasets.GeneratorBasedBuilder):
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"""train tickets"""
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BUILDER_CONFIGS = [
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TrainTicketsConfig(
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name="train_tickets-yu2020pick",
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version=datasets.Version("1.0.0"),
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description="Chinese train tickets",
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),
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]
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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("string"),
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"words": datasets.Sequence(datasets.Value("string")),
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"bboxes": datasets.Sequence(
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datasets.Sequence(datasets.Value("int64"))
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),
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"ner_tags": datasets.Sequence(
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datasets.features.ClassLabel(
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names=[
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"O",
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"S-DATE",
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"S-DESTINATION_STATION",
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"S-NAME",
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"S-SEAT_CATEGORY",
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"S-STARTING_STATION",
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"S-TICKET_NUM",
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"S-TICKET_RATES",
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"S-TRAIN_NUM",
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]
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)
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),
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"image_path": datasets.Value("string"),
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}
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),
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supervised_keys=None,
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homepage="https://github.com/wenwenyu/PICK-pytorch",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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downloaded_file = dl_manager.download_and_extract(
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"https://drive.google.com/uc?export=download&id=1o8JktPD7bS74tfjz-8dVcZq_uFS6YEGh"
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)
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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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"filelist": f"{downloaded_file}/train_tickets/synth1530_real320_baseline_trainset.csv"
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"filelist": f"{downloaded_file}/train_tickets/real80_baseline_testset.csv"
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},
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),
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]
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def _read_gt_file_with_box_entity_type(self, filepath: str):
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with open(filepath, "r", encoding="utf-8") as f:
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document_text = f.read()
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regex = (
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r"^\s*(-?\d+)\s*,\s*(-?\d+\.?\d*)\s*,\s*(-?\d+\.?\d*)\s*,\s*(-?\d+\.?\d*)\s*,\s*(-?\d+\.?\d*)\s*,"
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r"\s*(-?\d+\.?\d*)\s*,\s*(-?\d+\.?\d*)\s*,\s*(-?\d+\.?\d*)\s*,\s*(-?\d+\.?\d*)\s*,(.*),(.*)\n?$"
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)
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matches = re.finditer(regex, document_text, re.MULTILINE)
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res = []
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for _, match in enumerate(matches, start=1):
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points = [int(match.group(i)) for i in range(2, 10)]
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x = points[0:8:2]
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y = points[1:8:2]
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x1 = min(x)
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y1 = min(y)
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x2 = max(x)
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y2 = max(y)
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transcription = str(match.group(10))
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entity_type = str(match.group(11))
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res.append((x1, y1, x2, y2, transcription, entity_type))
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return res
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def _generate_examples(self, filelist):
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logger.info("⏳ Generating examples from = %s", filelist)
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ann_dir = os.path.join(os.path.dirname(filelist), "boxes_trans")
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img_dir = os.path.join(os.path.dirname(filelist), "images1930")
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print(ann_dir)
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with open(filelist) as csv_file:
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csv_reader = csv.reader(csv_file, delimiter=",")
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for row in csv_reader:
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guid = row[0]
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filename = row[2]
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words = []
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bboxes = []
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ner_tags = []
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file_path = os.path.join(ann_dir, f"{filename}.tsv")
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data = self._read_gt_file_with_box_entity_type(file_path)
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image_path = os.path.join(img_dir, f"{filename}.jpg")
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_, size = load_image(image_path)
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for item in data:
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box = item[0:4]
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transcription, label = item[4:6]
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words.append(transcription)
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bboxes.append(normalize_bbox(box, size))
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if label == "other":
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ner_tags.append("O")
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else:
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ner_tags.append("S-" + label.upper())
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yield guid, {
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"id": str(guid),
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"words": words,
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"bboxes": bboxes,
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"ner_tags": ner_tags,
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"image_path": image_path,
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}
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