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--- |
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dataset_info: |
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features: |
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- name: File |
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dtype: string |
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- name: Date |
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dtype: int64 |
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- name: Region_OCR |
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dtype: string |
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- name: Region_OCR_aligned |
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dtype: string |
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- name: Region_GT_aligned |
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dtype: string |
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- name: Sentence_OCR_aligned |
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dtype: string |
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- name: Sentence_GT_aligned |
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dtype: string |
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- name: Sentence_OCR |
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dtype: string |
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- name: Sentence_GT |
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dtype: string |
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- name: Distance |
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dtype: int64 |
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- name: CER |
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dtype: float64 |
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- name: WER |
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dtype: float64 |
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- name: Sentence_OCR_corrupted |
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dtype: string |
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- name: corrupted_cer |
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dtype: float64 |
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- name: corrupted_wer |
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dtype: float64 |
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splits: |
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- name: train |
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num_bytes: 18654472 |
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num_examples: 2632 |
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- name: dev |
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num_bytes: 2542628 |
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num_examples: 336 |
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- name: test |
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num_bytes: 2031987 |
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num_examples: 301 |
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download_size: 6813801 |
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dataset_size: 23229087 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: dev |
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path: data/dev-* |
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- split: test |
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path: data/test-* |
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--- |
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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. |
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- **Training Set**: 2.63k phrases |
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- **Validation Set**: 336 |
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- **Test Set**: 301 |
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## Purpose |
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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. |
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## Dataset Columns Description |
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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: |
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| Column Name | Data Type | Description | |
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|---------------------------|--------------|-----------------------------------------------------------------------------------------------| |
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| **File** | `string` | Name or identifier of the file associated with the OCR and GT data. | |
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| **Date** | `int64` | Date of the document, encoded as an integer. | |
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| **Region_OCR** | `string` | OCR-recognized region of text. | |
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| **Region_OCR_aligned** | `string` | Aligned OCR-recognized region for better comparison with GT data. | |
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| **Region_GT_aligned** | `string` | Aligned ground truth region for validation against OCR results. | |
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| **Sentence_OCR_aligned** | `string` | Aligned OCR-recognized sentences for improved accuracy in comparisons. | |
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| **Sentence_GT_aligned** | `string` | Aligned ground truth sentences corresponding to OCR results. | |
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| **Sentence_OCR** | `string` | Original OCR-recognized sentences, unaligned. | |
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| **Sentence_GT** | `string` | Original ground truth sentences, unaligned. | |
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| **Distance** | `int64` | The computed edit distance between OCR sentences and ground truth. | |
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| **CER** | `float64` | Character Error Rate (CER) for OCR sentences compared to the ground truth. Values range from 0 to 0.29. | |
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| **WER** | `float64` | Word Error Rate (WER) for OCR sentences compared to the ground truth. Values range from 0 to 1.5. | |
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| **Sentence_OCR_corrupted** | `string` | OCR sentences with additional corruption introduced for synthetic error analysis. | |
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| **corrupted_cer** | `float64` | Character Error Rate for corrupted OCR sentences compared to the ground truth. Values range from 0 to 0.35. | |
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| **corrupted_wer** | `float64` | Word Error Rate for corrupted OCR sentences compared to the ground truth. | |
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This structured dataset is designed to analyze and evaluate OCR post-correction performance with both real and synthetic data. |
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## Author Information |
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Prepared by **Mikhail Biriuchinskii**, an engineer in Natural Language Processing at Sorbonne University. |
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## Original Dataset Reference |
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For more information, visit the original dataset source: [ICDAR2017 Competition on Post-OCR Text Correction](http://l3i.univ-larochelle.fr/ICDAR2017PostOCR). |
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## Copyright |
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The original corpus is publicly accessible, and I do not hold any rights to this deployment of the corpus. |
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