README.md

---
license: mit
language:
- la
task_categories:
- image-to-text
pretty_name: HOME-Alcar-line
dataset_info:
  features:
  - name: image
    dtype: image
  - name: text
    dtype: string
  splits:
  - name: train
    num_examples: 59969
  - name: validation
    num_examples: 7905
  - name: test
    num_examples: 6932
  dataset_size: 74806
tags:
  - atr
  - htr
  - ocr
  - historical
  - handwritten
---

# HOME-Alcar - line level

## Table of Contents
- [HOME-Alcar - line level](#home-alcar-line-level)
  - [Table of Contents](#table-of-contents)
  - [Dataset Description](#dataset-description)
    - [Languages](#languages)
  - [Dataset Structure](#dataset-structure)
    - [Data Instances](#data-instances)
    - [Data Fields](#data-fields)

## Dataset Description

- **Homepage:** [HOME](https://www.heritageresearch-hub.eu/project/home/)
- **Source:** [Arkindex](https://demo.arkindex.org/browse/46b9b1f4-baeb-4342-a501-e2f15472a276?top_level=true&folder=true)
- **Point of Contact:** [TEKLIA](https://teklia.com)

## Dataset Summary 

The HOME-Alcar (Aligned and Annotated Cartularies) dataset is a Medieval corpus. The 17 medieval manuscripts in this corpus are cartularies, i.e. books copying charters and legal acts, produced between the 12th and 14th centuries. 

This dataset comes from the [following publication](https://doi.org/10.5281/zenodo.5600884):
`
Stutzmann, D., Torres Aguilar, S., & Chaffenet, P. (2021). HOME-Alcar: Aligned and Annotated Cartularies [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5600884
`

Note that all images are resized to a fixed height of 128 pixels.

### Languages

All the documents in the dataset are written in Latin.

## Dataset Structure

### Data Instances

```
{
  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4300x128 at 0x1A800E8E190,
  'text': 'quatre mille livres de tournoiz poiez, si com¬'
}
```

### Data Fields


- `image`: a PIL.Image.Image object containing the image. Note that when accessing the image column (using dataset[0]["image"]), the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0].
- `text`: the label transcription of the image.