NoisyOffice
Donated on 1/2/2015
Corpus intended to do cleaning (or binarization) and enhancement of noisy grayscale printed text images using supervised learning methods. Noisy images and their corresponding ground truth provided.
Dataset Characteristics
Multivariate
Subject Area
Computer Science
Associated Tasks
Classification, Regression
Feature Type
Real
# Instances
216
# Features
216
Dataset Information
Additional Information
AIMS AND PURPOSES This corpus is intended to do cleaning (or binarization) and enhancement of noisy grayscale printed text images using supervised learning methods. To this end, noisy images and their corresponding cleaned or binarized ground truth are provided. Double resolution ground truth images are also provided in order to test superresolution methods. CORPUS DIRECTORIES STRUCTURE SimulatedNoisyOffice folder has been prepared for training, validation and test of supervised methods. RealNoisyOffice folder is provided for subjective evaluation. . |-- RealNoisyOffice | |-- real_noisy_images_grayscale | `-- real_noisy_images_grayscale_doubleresolution `-- SimulatedNoisyOffice |-- clean_images_binaryscale |-- clean_images_grayscale |-- clean_images_grayscale_doubleresolution `-- simulated_noisy_images_grayscale RealNoisyOffice - real_noisy_images_grayscale: 72 grayscale images of scanned 'noisy' images. - real_noisy_images_grayscale_doubleresolution: idem, double resolution. SimulatedNoisyOffice - simulated_noisy_images_grayscale: 72 grayscale images of scanned 'simulated noisy' images for training, validation and test. - clean_images_grayscale_doubleresolution: Grayscale ground truth of the images with double resolution. - clean_images_grayscale: Grayscale ground truth of the images with smoothing on the borders (normal resolution). - clean_images_binary: Binary ground truth of the images (normal resolution). DESCRIPTION Every file is a printed text image following the pattern FontABC_NoiseD_EE.png: A) Size of the font: footnote size (f), normal size (n) o large size (L). B) Font type: typewriter (t), sans serif (s) or roman (r). C) Yes/no emphasized font (e/m). D) Type of noise: folded sheets (Noise f), wrinkled sheets (Noise w), coffee stains (Noise c), and footprints (Noise p). E) Data set partition: training (TR), validation (VA), test (TE), real (RE). For each type of font, one type of Noise: 17 files * 4 types of noise = 72 images. OTHER INFORMATION 200 ppi => normal resolution 400 ppi => double resolution
Has Missing Values?
No
Variable Information
The format of each file is the following: Grayscale PNG files (http://en.wikipedia.org/wiki/Portable_Network_Graphics). The ground truth is also provided as grayscale PNG files, and for the binary version the values are saturated to 0 and 255.
Dataset Files
| File | Size |
|---|---|
| NoisyOffice/RealNoisyOffice/real_noisy_images_grayscale_doubleresolution/FontLte_Noisew_RE.png | 6.2 MB |
| NoisyOffice/RealNoisyOffice/real_noisy_images_grayscale_doubleresolution/FontLte_Noisef_RE.png | 5.8 MB |
| NoisyOffice/RealNoisyOffice/real_noisy_images_grayscale_doubleresolution/FontLte_Noisec_RE.png | 5.8 MB |
| NoisyOffice/RealNoisyOffice/real_noisy_images_grayscale_doubleresolution/FontLte_Noisep_RE.png | 5.7 MB |
| NoisyOffice/RealNoisyOffice/real_noisy_images_grayscale_doubleresolution/FontLtm_Noisef_RE.png | 5.6 MB |
0 to 5 of 528
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset noisyoffice = fetch_ucirepo(id=318) # data (as pandas dataframes) X = noisyoffice.data.features y = noisyoffice.data.targets # metadata print(noisyoffice.metadata) # variable information print(noisyoffice.variables)
Espaa-Boquera, S., Pastor-Pellicer, J., Castro-Bleda, M., & Zamora-Martinez, F. (2007). NoisyOffice [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5G31N.
Creators
S. Espaa-Boquera
J. Pastor-Pellicer
M.J. Castro-Bleda
F. Zamora-Martinez
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.