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