Datasets:
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---
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_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: 11573638
num_examples: 765
- name: dev
num_bytes: 1056634
num_examples: 95
- name: test
num_bytes: 1782846
num_examples: 97
download_size: 9457542
dataset_size: 14413118
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. |