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---
dataset_info:
features:
- name: File
dtype: string
- name: Date
dtype: int64
- name: Region_OCR
dtype: string
- name: Region_OCR_aligned
dtype: string
- name: Region_GT_aligned
dtype: string
- name: Sentence_OCR_aligned
dtype: string
- name: Sentence_GT_aligned
dtype: string
- name: Sentence_OCR
dtype: string
- name: Sentence_GT
dtype: string
- name: Distance
dtype: int64
- name: CER
dtype: float64
- name: WER
dtype: float64
- name: Sentence_OCR_corrupted
dtype: string
- name: corrupted_cer
dtype: float64
- name: corrupted_wer
dtype: float64
splits:
- name: train
num_bytes: 18654472
num_examples: 2632
- name: dev
num_bytes: 2542628
num_examples: 336
- name: test
num_bytes: 2031987
num_examples: 301
download_size: 6813801
dataset_size: 23229087
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
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.
- **Training Set**: 2.63k phrases
- **Validation Set**: 336
- **Test Set**: 301
## 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.
## Dataset Columns Description
This dataset contains several columns with detailed information about OCR (Optical Character Recognition) outputs and their corresponding ground truths (GT). Below is a description of each column:
| Column Name | Data Type | Description |
|---------------------------|--------------|-----------------------------------------------------------------------------------------------|
| **File** | `string` | Name or identifier of the file associated with the OCR and GT data. |
| **Date** | `int64` | Date of the document, encoded as an integer. |
| **Region_OCR** | `string` | OCR-recognized region of text. |
| **Region_OCR_aligned** | `string` | Aligned OCR-recognized region for better comparison with GT data. |
| **Region_GT_aligned** | `string` | Aligned ground truth region for validation against OCR results. |
| **Sentence_OCR_aligned** | `string` | Aligned OCR-recognized sentences for improved accuracy in comparisons. |
| **Sentence_GT_aligned** | `string` | Aligned ground truth sentences corresponding to OCR results. |
| **Sentence_OCR** | `string` | Original OCR-recognized sentences, unaligned. |
| **Sentence_GT** | `string` | Original ground truth sentences, unaligned. |
| **Distance** | `int64` | The computed edit distance between OCR sentences and ground truth. |
| **CER** | `float64` | Character Error Rate (CER) for OCR sentences compared to the ground truth. Values range from 0 to 0.29. |
| **WER** | `float64` | Word Error Rate (WER) for OCR sentences compared to the ground truth. Values range from 0 to 1.5. |
| **Sentence_OCR_corrupted** | `string` | OCR sentences with additional corruption introduced for synthetic error analysis. |
| **corrupted_cer** | `float64` | Character Error Rate for corrupted OCR sentences compared to the ground truth. Values range from 0 to 0.35. |
| **corrupted_wer** | `float64` | Word Error Rate for corrupted OCR sentences compared to the ground truth. |
This structured dataset is designed to analyze and evaluate OCR post-correction performance with both real and synthetic data.
## 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.
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