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---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: ocr
list:
- name: box
sequence:
sequence: float64
- name: label
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 267317016.0
num_examples: 652
download_size: 217146103
dataset_size: 267317016.0
---
# Dataset Card for "sroie_document_understanding"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Contributions](#contributions)
## Dataset Description
This dataset is an enriched version of SROIE 2019 dataset with additional labels for line descriptions and line totals for OCR and layout understanding.
## Dataset Structure
```python
DatasetDict({
train: Dataset({
features: ['image', 'ocr'],
num_rows: 652
})
})
```
### Data Fields
```python
{
'image': PIL Image object,
'ocr': [
# text box 1
{
'box': [[float, float], [float, float], [float, float], [float, float]],
'label': str, # "other" | "company" | "address" | "date" | "line_description" | "line_total" | "total"
'text': str
},
...
# text box N
{
'box': [[float, float], [float, float], [float, float], [float, float]],
'label': str,
'text': str,
}
]
}
```
## Dataset Creation
### Source Data
The dataset was obtained from [ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction](https://rrc.cvc.uab.es/?ch=13)
### Annotations
#### Annotation process
Additional labels to receipt line items were added using open source [labelme](https://github.com/wkentaro/labelme) tool.
#### Who are the annotators?
Arvind Rajan
## Additional Information
### Licensing Information
MIT License
### Contributions
Thanks to [@arvindrajan92](https://github.com/arvindrajan92) for adding this dataset.