mirror of https://github.com/open-mmlab/mmocr.git
* added sroie/metafile.yml * add sample_anno.md and textdet.py * modify and add all * fix lint * fix lint * fix lint * Update mmocr/datasets/preparers/data_converpyter. Co-authored-by: Tong Gao <gaotongxiao@gmail.com> * fix the reviewed * add comment of try to sroie_parser.py * modify data_obtainer.py * fix lint errors * fix download link Co-authored-by: Tong Gao <gaotongxiao@gmail.com>pull/1651/head
Ferry Huang
GitHub
parent
b79382cd6b
commit
1413b5043a
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Name: 'Scanned Receipts OCR and Information Extraction'
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Paper:
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Title: ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction
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URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8977955
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Venue: ICDAR
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Year: '2019'
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BibTeX: '@INPROCEEDINGS{8977955,
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author={Huang, Zheng and Chen, Kai and He, Jianhua and Bai, Xiang and Karatzas, Dimosthenis and Lu, Shijian and Jawahar, C. V.},
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booktitle={2019 International Conference on Document Analysis and Recognition (ICDAR)},
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title={ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction},
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year={2019},
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volume={},
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number={},
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pages={1516-1520},
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doi={10.1109/ICDAR.2019.00244}}'
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Data:
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Website: https://rrc.cvc.uab.es/?ch=13
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Language:
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- English
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Scene:
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- Document
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Granularity:
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- Word
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Tasks:
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- textdet
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- textrecog
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- textspotting
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License:
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Type: CC BY 4.0
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Link: https://creativecommons.org/licenses/by/4.0/
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Format: .txt
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**Text Detection, Text Recognition and Text Spotting**
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```text
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# x1,y1,x2,y2,x3,y3,x4,y4,trans
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72,25,326,25,326,64,72,64,TAN WOON YANN
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50,82,440,82,440,121,50,121,BOOK TA .K(TAMAN DAYA) SDN BND
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205,121,285,121,285,139,205,139,789417-W
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```
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data_root = 'data/sroie'
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cache_path = 'data/cache'
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data_obtainer = dict(
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type='NaiveDataObtainer',
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cache_path=cache_path,
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data_root=data_root,
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files=[
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dict(
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url='https://download.openmmlab.com/mmocr/data/'
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'sroie/0325updated.task1train(626p).zip',
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save_name='0325updated.task1train(626p).zip',
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md5='16137490f6865caac75772b9111d348c',
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split=['train'],
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content=['image', 'annotation'],
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mapping=[[
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'0325updated/0325updated.task1train(626p)/*.jpg',
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'textdet_imgs/train'
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],
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[
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'0325updated/0325updated.task1train(626p)/*.txt',
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'annotations/train'
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]]),
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dict(
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url='https://download.openmmlab.com/mmocr/data/'
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'sroie/task1&2_test(361p).zip',
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save_name='task1&2_test(361p).zip',
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md5='1bde54705db0995c57a6e34cce437fea',
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split=['test'],
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content=['image'],
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mapping=[[
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'task1&2_test(361p)/fulltext_test(361p)', 'textdet_imgs/test'
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]]),
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dict(
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url='https://download.openmmlab.com/mmocr/data/sroie/text.zip',
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save_name='text.zip',
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md5='8c534653f252ff4d3943fa27a956a74b',
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split=['test'],
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content=['annotation'],
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mapping=[['text', 'annotations/test']]),
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])
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data_converter = dict(
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type='TextDetDataConverter',
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splits=['train', 'test'],
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data_root=data_root,
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gatherer=dict(
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type='pair_gather',
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suffixes=['.jpg'],
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rule=[r'X(\d+)\.([jJ][pP][gG])', r'X\1.txt']),
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parser=dict(type='SROIETextDetAnnParser', encoding='utf-8-sig'),
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dumper=dict(type='JsonDumper'),
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delete=['text', 'task1&2_test(361p)', '0325updated', 'annotations'])
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config_generator = dict(type='TextDetConfigGenerator', data_root=data_root)
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_base_ = ['textdet.py']
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data_converter = dict(type='TextRecogCropConverter')
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config_generator = dict(type='TextRecogConfigGenerator')
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_base_ = ['textdet.py']
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data_converter = dict(type='TextSpottingDataConverter')
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config_generator = dict(type='TextSpottingConfigGenerator')
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@ -177,6 +177,8 @@ class BaseDataConverter:
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"""
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files = list()
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for file in list_files(img_path, suffixes):
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if not re.match(rule[0], osp.basename(file)):
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continue
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file2 = re.sub(rule[0], rule[1], osp.basename(file))
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file2 = file.replace(osp.basename(file), file2)
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file2 = file2.replace(self.img_dir, 'annotations')
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@ -4,6 +4,7 @@ from .funsd_parser import FUNSDTextDetAnnParser
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from .icdar_txt_parser import (ICDARTxtTextDetAnnParser,
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ICDARTxtTextRecogAnnParser)
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from .naf_parser import NAFAnnParser
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from .sroie_parser import SROIETextDetAnnParser
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from .svt_parser import SVTTextDetAnnParser
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from .totaltext_parser import TotaltextTextDetAnnParser
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from .wildreceipt_parser import WildreceiptKIEAnnParser
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@ -12,5 +13,5 @@ __all__ = [
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'ICDARTxtTextDetAnnParser', 'ICDARTxtTextRecogAnnParser',
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'TotaltextTextDetAnnParser', 'WildreceiptKIEAnnParser',
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'COCOTextDetAnnParser', 'SVTTextDetAnnParser', 'FUNSDTextDetAnnParser',
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'NAFAnnParser'
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'SROIETextDetAnnParser', 'NAFAnnParser'
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]
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# Copyright (c) OpenMMLab. All rights reserved.
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from typing import List, Optional, Tuple
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from mmocr.utils import bbox2poly
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from ..data_preparer import DATA_PARSERS
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from .base import BaseParser
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@DATA_PARSERS.register_module()
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class SROIETextDetAnnParser(BaseParser):
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"""SROIE Txt Format Text Detection Annotation Parser.
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The original annotation format of this dataset is stored in txt files,
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which is formed as the following format:
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x1, y1, x2, y2, x3, y3, x4, y4, transcription
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Args:
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separator (str): The separator between each element in a line. Defaults
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to ','.
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ignore (str): The text to be ignored. Defaults to '###'.
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format (str): The format of the annotation. Defaults to
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'x1,y1,x2,y2,x3,y3,x4,trans'.
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encoding (str): The encoding of the annotation file. Defaults to
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'utf-8-sig'.
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nproc (int): The number of processes to parse the annotation. Defaults
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to 1.
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remove_strs (List[str], Optional): Used to remove redundant strings in
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the transcription. Defaults to None.
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mode (str, optional): The mode of the box converter. Supported modes
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are 'xywh' and 'xyxy'. Defaults to None.
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"""
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def __init__(self,
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separator: str = ',',
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ignore: str = '###',
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format: str = 'x1,y1,x2,y2,x3,y3,x4,y4,trans',
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encoding: str = 'utf-8-sig',
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nproc: int = 1,
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remove_strs: Optional[List[str]] = None,
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mode: str = None) -> None:
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self.sep = separator
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self.format = format
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self.encoding = encoding
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self.ignore = ignore
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self.mode = mode
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self.remove_strs = remove_strs
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super().__init__(nproc=nproc)
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def parse_file(self, file: Tuple, split: str) -> Tuple:
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"""Parse single annotation."""
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img_file, txt_file = file
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instances = list()
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try:
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# there might be some illegal symbols in the annotation
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# which cannot be parsed by loader
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for anno in self.loader(txt_file, self.sep, self.format,
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self.encoding):
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anno = list(anno.values())
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if self.remove_strs is not None:
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for strs in self.remove_strs:
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for i in range(len(anno)):
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if strs in anno[i]:
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anno[i] = anno[i].replace(strs, '')
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poly = list(map(float, anno[0:-1]))
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if self.mode is not None:
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poly = bbox2poly(poly, self.mode)
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poly = poly.tolist()
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text = anno[-1]
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instances.append(
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dict(poly=poly, text=text, ignore=text == self.ignore))
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except Exception:
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pass
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return img_file, instances
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