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We introduce a novel dataset called CORD, which stands for a COnsolidated Receipt Dataset for post-OCR parsing.
Abstract [paper]
OCR is inevitably linked to NLP since its final output is in text. Advances in document intelligence are driving the need for a unified technology that integrates OCR with various NLP tasks, especially semantic parsing. Since OCR and semantic parsing have been studied as separate tasks so far, the datasets for each task on their own are rich, while those for the integrated post-OCR parsing tasks are relatively insufficient. In this study, we publish a consolidated dataset for receipt parsing as the first step towards post-OCR parsing tasks. The dataset consists of thousands of Indonesian receipts, which contains images and box/text annotations for OCR, and multi-level semantic labels for parsing. The proposed dataset can be used to address various OCR and parsing tasks.
- CORD v2 data has been uploaded to the Hugging Face Datasets. We investigated all data and corrected the incorrect labels. Also, we added the attribute
sub_group_idto each element ofvalid_line. We can use this information to describe more accurate hierarchy of the resulting parse. See thegt_parseof the examples containing themenu.sub_nm, and compare those of the CORD v1. [20220720] - CORD v1 data has been uploaded to the Hugging Face Datasets. CORD v1 has the same contents as v0 except the
gt_parseattribute.gt_parserepresents theparseformat constructed from thevalid_line. [20220720] - 1,000 sample dataset will be available soon. Some class labels shown in the original paper were removed due to Indonesian legal issues. In particular, the
store_info,payment_info, andetcfields have been removed from the target class to be published. [20191212] - 1,000 sample dataset has been released. [20191226]
- Some categories not used in the current dataset have been removed from the class definition. [20200210]
- Large Scale: over 11,000 Indonesian receipts collected from shops and restaurants
- Fine-grained classes: five superclass and 42 subclass labels
- Multi hierarchy: includes group annotations
- Additional information: line group (
row_id), region of interest (roi), cut lines (repeating_symbol), andis_keyflag
| No | Category | Tag field (subclasses) | Description |
|---|---|---|---|
| 1 | menu (14) | menu.nm | name of menu |
| 2 | menu.num | identification # of menu | |
| 3 | menu.unitprice | unit price of menu | |
| 4 | menu.cnt | quantity of menu | |
| 5 | menu.discountprice | discounted price of menu | |
| 6 | menu.price | total price of menu | |
| 7 | menu.itemsubtotal | price of each menu after discount applied | |
| 8 | menu.vatyn | whether the price includes tax or not | |
| 9 | menu.etc | others | |
| 10 | menu.sub_nm | name of submenu | |
| 11 | |||
| 12 | menu.sub_unitprice | unit price of submenu | |
| 13 | menu.sub_cnt | quantity of submenu | |
| 14 | |||
| 15 | menu.sub_price | total price of submenu | |
| 16 | menu.sub_etc | others | |
| 17 | void menu (2) | void_menu.nm | name of menu |
| 18 | |||
| 19 | |||
| 20 | |||
| 21 | void_menu.price | total price of menu | |
| 22 | |||
| 23 | subtotal (6) | subtotal.subtotal_price | subtotal price |
| 24 | subtotal.discount_price | discounted price in total | |
| 25 | |||
| 26 | subtotal.service_price | service charge | |
| 27 | subtotal.othersvc_price | added charge other than service charge | |
| 28 | subtotal.tax_price | tax amount | |
| 29 | |||
| 30 | subtotal.etc | others | |
| 31 | void total (0) | ||
| 32 | |||
| 33 | |||
| 34 | |||
| 35 | total (8) | total.total_price | total price |
| 36 | total.total_etc | others | |
| 37 | total.cashprice | amount of price paid in cash | |
| 38 | total.changeprice | amount of change in cash | |
| 39 | total.creditcardprice | amount of price paid in credit/debit card | |
| 40 | total.emoneyprice | amount of price paid in emoney, point | |
| 41 | total.menutype_cnt | total count of type of menu | |
| 42 | total.menuqty_cnt | total count of quantity |
| Attribute Name | Description | ||
|---|---|---|---|
| valid_line | words | quad | Four coordinates of quadrilateral |
| is_key | Flag to indicates the text used as a key or not | ||
| row_id | Line index | ||
| text | Incorporating text of the corresponding box | ||
| category | Parse class label | ||
| group_id | Group id to which the valid_line belongs | ||
| ---------------- | ---------- | ------ | ---------------------------------------------------------- |
| meta | version | Dataset version | |
| image_id | Corresponding image id | ||
| split | 'train' or 'dev' or 'test' | ||
| image_size | Size of the image (by pixel) | ||
| ---------------- | ---------- | ------ | ---------------------------------------------------------- |
| roi* | Four coordinates that encompass the area of receipt region | ||
| ---------------- | ---------- | ------ | ---------------------------------------------------------- |
| repeating_symbol | quad | Four coordinates of quadrilateral | |
| text | = or - or . or etc. |
*A blank 'roi' value means the entire area of the image.
| Version | Name | Total | # train | # dev | # test | release date |
|---|---|---|---|---|---|---|
| v0 | sample (zip) | 1,000 | 800 | 100 | 100 | 26 Dec 2019 |
| v1 | Hugging Face Datasets Link | 1,000 | 800 | 100 | 100 | 20 Jul 2022 |
| v2 | Hugging Face Datasets Link | 1,000 | 800 | 100 | 100 | 20 Jul 2022 |
@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}
}
@article{hwang2019post,
title={Post-OCR parsing: building simple and robust parser via BIO tagging},
author={Hwang, Wonseok and Kim, Seonghyeon and Yim, Jinyeong and Seo, Minjoon and Park, Seunghyun and Park, Sungrae and Lee, Junyeop and Lee, Bado and Lee, Hwalsuk}
booktitle={Document Intelligence Workshop at Neural Information Processing Systems}
year={2019}
}
@article{kim2021donut,
title={OCR-free Document Understanding Transformer},
author={Kim, Geewook and Hong, Teakgyu and Yim, Moonbin and Nam, JeongYeon and Park, Jinyoung and Yim, Jinyeong and Hwang, Wonseok and Yun, Sangdoo and Han, Dongyoon and Park, Seunghyun},
journal={arXiv preprint arXiv:2111.15664},
year={2021}
}