m-biriuchinskii/ICDAR2017-filtered-1800-1900

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.