README.md

---
dataset_info:
  features:
  - name: File
    dtype: string
  - name: Date
    dtype: int64
  - name: OCR_toInput
    dtype: string
  - name: OCR_aligned
    dtype: string
  - name: GS_aligned
    dtype: string
  - name: Ground_truth
    dtype: string
  - name: distance
    dtype: int64
  - name: cer
    dtype: float64
  - name: wer
    dtype: float64
  splits:
  - name: train
    num_bytes: 9262494
    num_examples: 765
  - name: dev
    num_bytes: 845982
    num_examples: 95
  - name: test
    num_bytes: 1426898
    num_examples: 97
  download_size: 7570886
  dataset_size: 11535374
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: dev
    path: data/dev-*
  - split: test
    path: data/test-*
task_categories:
- image-to-text
language:
- fr
tags:
- OCR
- NLP
- TAL
pretty_name: Split ICDAR2017 dataset
---
This dataset is a filtered version of the *ICDAR2017* Competition on Handwritten Text Recognition, focusing on monograph texts written between 1800 and 1900. It consists of a total of **957 documents**, divided into training, validation, and testing sets, and is designed for post-correction of OCR (Optical Character Recognition) text.

- **Total Documents**: 957
  - **Training Set**: 765
  - **Validation Set**: 95
  - **Test Set**: 97

## Purpose
The dataset aims to improve the accuracy of digitized texts by providing a reliable Ground Truth for comparison and correction, specifically addressing the challenges of French text of 19th century.

## Structure
The dataset is organized as follows:

```plaintext
dataset/
├── train/
│   ├── file1.txt
│   ├── file2.txt
│   └── ...
├── dev/
│   ├── file1.txt
│   ├── file2.txt
│   └── ...
├── test/
│   ├── file1.txt
│   ├── file2.txt
│   └── ...
└── metadata.csv  # This file contains metadata for each txt file
```


- **Content** [#.txt]
  - **1st line**: "[OCR_toInput] " => Raw OCRed text to be denoised.
  - **2nd line**: "[OCR_aligned] " => Aligned OCRed text.
  - **3rd line**: "[GS_aligned] " => Aligned Gold Standard.

The alignment was made at the character level using "@" symbols. "#" symbols correspond to the absence of GS  either related to alignment uncertainities or related to unreadable characters in the source document. 
For a better view of the alignment, make sure to disable the "word wrap" option in your text editor.

## Author Information
Prepared by **Mikhail Biriuchinskii**, an engineer in Natural Language Processing at Sorbonne University.

## Original Dataset Reference
For more information, visit the original dataset source: [ICDAR2017 Competition on Post-OCR Text Correction](http://l3i.univ-larochelle.fr/ICDAR2017PostOCR).

## Copyright
The original corpus is publicly accessible, and I do not hold any rights to this deployment of the corpus.