Datasets:
File size: 1,945 Bytes
487a979 6f2d8d7 487a979 4431964 487a979 310ac00 1c1bf02 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
---
dataset_info:
features:
- name: corpus
dtype: string
- name: book
dtype: string
- name: date
dtype: int32
- name: filename
dtype: string
- name: transcription
dtype: string
- name: aadi
dtype: string
- name: paddleocr
dtype: string
- name: tesseract
dtype: string
- name: transkribus
dtype: string
- name: img
dtype: image
- name: complex_layout
dtype: bool
- name: language
dtype: string
splits:
- name: train
num_examples: 3782
configs:
- config_name: default
data_files:
- split: train
path: data/PORTO-part-*
license: cc-by-4.0
task_categories:
- image-to-text
- fill-mask
- text-generation
language:
- pt
tags:
- OCR
- Post-OCR
- Historical
size_categories:
- 1K<n<10k
pretty_name: Corpus / Book / Date / Filename / Transcription / AADI /
PaddleOCR / Tesseract / Transkribus / Image / Complex Layout / Language
---
# **P**ost-**O**CR **R**esources for **T**ext **O**ptimisation
Resource for evaluation and develop OCRs and Post-OCR focused on historical Portuguese.
How to load the dataset:
```
from datasets import load_dataset
dataset = load_dataset("LIACC/PORTO")
```
# Citation
When using or citing this corpus, kindly cite the following publication:
```
@inproceedings{10.1145/3746252.3761633,
author = {Freitas Os\'{o}rio, Tom\'{a}s and Lopes Cardoso, Henrique},
title = {Portuguese post-OCR Resources for Text Optimisation},
year = {2025},
isbn = {9798400720406},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3746252.3761633},
doi = {10.1145/3746252.3761633},
booktitle = {Proceedings of the 34th ACM International Conference on Information and Knowledge Management},
pages = {6361–6366},
numpages = {6},
keywords = {historical portuguese corpora, ocr evaluation, post-ocr correction resources},
location = {Seoul, Republic of Korea},
series = {CIKM '25}
}
```
|