Datasets:

biglam
/

yalta_ai_tabular_dataset

	---
	annotations_creators:
	- expert-generated
	language: []
	language_creators:
	- expert-generated
	license:
	- cc-by-4.0
	multilinguality: []
	pretty_name: YALTAi Tabular Dataset
	size_categories:
	- n<1K
	source_datasets: []
	tags:
	- manuscripts
	- lam
	task_categories:
	- object-detection
	task_ids: []
	---

	# YALTAi Tabular Dataset

	## Table of Contents
	- [YALTAi Tabular Dataset](#YALTAi-Tabular-Dataset)
	- [Table of Contents](#table-of-contents)
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
	- [Who are the source language producers?](#who-are-the-source-language-producers)
	- [Annotations](#annotations)
	- [Annotation process](#annotation-process)
	- [Who are the annotators?](#who-are-the-annotators)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- Homepage: [https://doi.org/10.5281/zenodo.6827706](https://doi.org/10.5281/zenodo.6827706)
	- Paper: [https://arxiv.org/abs/2207.11230](https://arxiv.org/abs/2207.11230)

	### Dataset Summary

	This dataset contains a subset of data used in the paper [You Actually Look Twice At it (YALTAi): using an object detectionapproach instead of region segmentation within the Kraken engine](https://arxiv.org/abs/2207.11230). This paper proposes treating page layout recognition on historical documents as an object detection task (compared to the usual pixel segmentation approach). This dataset covers pages with tabular information with the following objects "Header", "Col", "Marginal", "text".

	### Supported Tasks and Leaderboards

	- `object-detection`: This dataset can be used to train a model for object-detection on historic document images.


	## Dataset Structure

	This dataset has two configurations. These configurations both cover the same data and annotations but provide these annotations in different forms to make it easier to intergrate the data with existing processing pipelines.

	- The first configuration `YOLO` uses the original format of the data.
	- The second configuration converts the YOLO format into a format which is closer to the `COCO` annotation format. This is done in particular to make it easier to work with the `feature_extractor`s from the `Transformers` models for object detection which expect data to be in a COCO style format.

	### Data Instances

	Provide an JSON-formatted example and brief description of a typical instance in the dataset. If available, provide a link to further examples.

	An example instance from the COCO config:

	```
	{'height': 2944,
	'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=2064x2944 at 0x7FA413CDA210>,
	'image_id': 0,
	'objects': [{'area': 435956,
	'bbox': [0.0, 244.0, 1493.0, 292.0],
	'category_id': 0,
	'id': 0,
	'image_id': '0',
	'iscrowd': False,
	'segmentation': []},
	{'area': 88234,
	'bbox': [305.0, 127.0, 562.0, 157.0],
	'category_id': 2,
	'id': 0,
	'image_id': '0',
	'iscrowd': False,
	'segmentation': []},
	{'area': 5244,
	'bbox': [1416.0, 196.0, 92.0, 57.0],
	'category_id': 2,
	'id': 0,
	'image_id': '0',
	'iscrowd': False,
	'segmentation': []},
	{'area': 5720,
	'bbox': [1681.0, 182.0, 88.0, 65.0],
	'category_id': 2,
	'id': 0,
	'image_id': '0',
	'iscrowd': False,
	'segmentation': []},
	{'area': 374085,
	'bbox': [0.0, 540.0, 163.0, 2295.0],
	'category_id': 1,
	'id': 0,
	'image_id': '0',
	'iscrowd': False,
	'segmentation': []},
	{'area': 577599,
	'bbox': [104.0, 537.0, 253.0, 2283.0],
	'category_id': 1,
	'id': 0,
	'image_id': '0',
	'iscrowd': False,
	'segmentation': []},
	{'area': 598670,
	'bbox': [304.0, 533.0, 262.0, 2285.0],
	'category_id': 1,
	'id': 0,
	'image_id': '0',
	'iscrowd': False,
	'segmentation': []},
	{'area': 56,
	'bbox': [284.0, 539.0, 8.0, 7.0],
	'category_id': 1,
	'id': 0,
	'image_id': '0',
	'iscrowd': False,
	'segmentation': []},
	{'area': 1868412,
	'bbox': [498.0, 513.0, 812.0, 2301.0],
	'category_id': 1,
	'id': 0,
	'image_id': '0',
	'iscrowd': False,
	'segmentation': []},
	{'area': 307800,
	'bbox': [1250.0, 512.0, 135.0, 2280.0],
	'category_id': 1,
	'id': 0,
	'image_id': '0',
	'iscrowd': False,
	'segmentation': []},
	{'area': 494109,
	'bbox': [1330.0, 503.0, 217.0, 2277.0],
	'category_id': 1,
	'id': 0,
	'image_id': '0',
	'iscrowd': False,
	'segmentation': []},
	{'area': 52,
	'bbox': [1734.0, 1013.0, 4.0, 13.0],
	'category_id': 1,
	'id': 0,
	'image_id': '0',
	'iscrowd': False,
	'segmentation': []},
	{'area': 90666,
	'bbox': [0.0, 1151.0, 54.0, 1679.0],
	'category_id': 1,
	'id': 0,
	'image_id': '0',
	'iscrowd': False,
	'segmentation': []}],
	'width': 2064}
	```

	An example instance from the YOLO config:

	``` python
	{'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=2064x2944 at 0x7FAA140F2450>,
	'objects': {'bbox': [[747, 390, 1493, 292],
	[586, 206, 562, 157],
	[1463, 225, 92, 57],
	[1725, 215, 88, 65],
	[80, 1688, 163, 2295],
	[231, 1678, 253, 2283],
	[435, 1675, 262, 2285],
	[288, 543, 8, 7],
	[905, 1663, 812, 2301],
	[1318, 1653, 135, 2280],
	[1439, 1642, 217, 2277],
	[1737, 1019, 4, 13],
	[26, 1991, 54, 1679]],
	'label': [0, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1]}}
	```


	Provide any additional information that is not covered in the other sections about the data here. In particular describe any relationships between data points and if these relationships are made explicit.


	### Data Fields

	The fields for the YOLO config:

	- `image`: the image
	- `objects`: the annotations which consits of:
	- `bbox`: a list of bounding boxes for the image
	- `label`: a list of labels for this image

	The fields for the COCO config:

	- `heigh`: height of the image
	- `width`: width of the image
	- `image`: image
	- `image_id`: id for the image
	- `objects`: annotations in COCO format, consisting of a list containing dictionaries with the follwoing keys:
	- `bbox`: bounding boxes for the images
	- `category_id`: label for the image
	- `image_id`: id for the image
	- `iscrowd`: COCO is crowd flag
	- `segmentation`: COCO segmentation annotations (empty in this case but kept for compatibiality with other processing scripts



	### Data Splits

	Describe and name the splits in the dataset if there are more than one.

	Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g. if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here.

	Provide the sizes of each split. As appropriate, provide any descriptive statistics for the features, such as average length. For example:

	\| \| train \| validation \| test \|
	\|-------------------------\|------:\|-----------:\|-----:\|
	\| Input Sentences \| \| \| \|
	\| Average Sentence Length \| \| \| \|

	## Dataset Creation

	### Curation Rationale

	What need motivated the creation of this dataset? What are some of the reasons underlying the major choices involved in putting it together?

	### Source Data

	This section describes the source data (e.g. news text and headlines, social media posts, translated sentences,...)

	#### Initial Data Collection and Normalization

	Describe the data collection process. Describe any criteria for data selection or filtering. List any key words or search terms used. If possible, include runtime information for the collection process.

	If data was collected from other pre-existing datasets, link to source here and to their [Hugging Face version](https://huggingface.co/datasets/dataset_name).

	If the data was modified or normalized after being collected (e.g. if the data is word-tokenized), describe the process and the tools used.

	#### Who are the source language producers?

	State whether the data was produced by humans or machine generated. Describe the people or systems who originally created the data.

	If available, include self-reported demographic or identity information for the source data creators, but avoid inferring this information. Instead state that this information is unknown. See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender.

	Describe the conditions under which the data was created (for example, if the producers were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.

	Describe other people represented or mentioned in the data. Where possible, link to references for the information.

	### Annotations

	If the dataset contains annotations which are not part of the initial data collection, describe them in the following paragraphs.

	#### Annotation process

	If applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes.

	#### Who are the annotators?

	If annotations were collected for the source data (such as class labels or syntactic parses), state whether the annotations were produced by humans or machine generated.

	Describe the people or systems who originally created the annotations and their selection criteria if applicable.

	If available, include self-reported demographic or identity information for the annotators, but avoid inferring this information. Instead state that this information is unknown. See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender.

	Describe the conditions under which the data was annotated (for example, if the annotators were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.

	### Personal and Sensitive Information

	State whether the dataset uses identity categories and, if so, how the information is used. Describe where this information comes from (i.e. self-reporting, collecting from profiles, inferring, etc.). See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender. State whether the data is linked to individuals and whether those individuals can be identified in the dataset, either directly or indirectly (i.e., in combination with other data).

	State whether the dataset contains other data that might be considered sensitive (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history).

	If efforts were made to anonymize the data, describe the anonymization process.

	## Considerations for Using the Data

	### Social Impact of Dataset

	Please discuss some of the ways you believe the use of this dataset will impact society.

	The statement should include both positive outlooks, such as outlining how technologies developed through its use may improve people's lives, and discuss the accompanying risks. These risks may range from making important decisions more opaque to people who are affected by the technology, to reinforcing existing harmful biases (whose specifics should be discussed in the next section), among other considerations.

	Also describe in this section if the proposed dataset contains a low-resource or under-represented language. If this is the case or if this task has any impact on underserved communities, please elaborate here.

	### Discussion of Biases

	Provide descriptions of specific biases that are likely to be reflected in the data, and state whether any steps were taken to reduce their impact.

	For Wikipedia text, see for example [Dinan et al 2020 on biases in Wikipedia (esp. Table 1)](https://arxiv.org/abs/2005.00614), or [Blodgett et al 2020](https://www.aclweb.org/anthology/2020.acl-main.485/) for a more general discussion of the topic.

	If analyses have been run quantifying these biases, please add brief summaries and links to the studies here.

	### Other Known Limitations

	If studies of the datasets have outlined other limitations of the dataset, such as annotation artifacts, please outline and cite them here.

	## Additional Information

	### Dataset Curators

	List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.

	### Licensing Information

	Provide the license and link to the license webpage if available.

	### Citation Information


	```
	@dataset{clerice_thibault_2022_6827706,
	author = {Clérice, Thibault},
	title = {YALTAi: Tabular Dataset},
	month = jul,
	year = 2022,
	publisher = {Zenodo},
	version = {1.0.0},
	doi = {10.5281/zenodo.6827706},
	url = {https://doi.org/10.5281/zenodo.6827706}
	}
	```

	If the dataset has a [DOI](https://doi.org/10.5281/zenodo.6827706), please provide it here.

	### Contributions

	Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.