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NoisyOffice Data Set
Download: Data Folder, Data Set Description

Abstract: 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.

Data Set Characteristics:  


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Attribute Characteristics:


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Date Donated


Associated Tasks:

Classification, Regression

Missing Values?


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M.J. Castro-Bleda (1), S. España-Boquera (1), J. Pastor-Pellicer (1), F. Zamora-Martinez (2)
mcastro '@', sespana '@', jpastor '@', francisco.zamora '@'

(1) Departamento de Sistemas Informáticos  y Computación, Universitat Politècnica  de València, Valencia, Spain
(2) Departamento de Ciencias Físicas, Matemáticas y de la Computación, Universidad CEU Cardenal Herrera, Alfara del Patriarca, València, Spain

Data Set Information:


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.


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

- real_noisy_images_grayscale: 72 grayscale images of scanned 'noisy' images.
- real_noisy_images_grayscale_doubleresolution: idem, double resolution.

- 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).


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.


200 ppi => normal resolution
400 ppi => double resolution

Attribute Information:

The format of each file is the following: Grayscale PNG files ([Web Link]). The ground truth is also provided as grayscale PNG files, and for the binary version the values are saturated to 0 and 255.

Relevant Papers:

J. L. Adelantado­Torres, J. Pastor­Pellicer, and M. J. Castro­Bleda. Una aplicación móvil para la captura y
mejora de imágenes de textos, in: V Jornadas TIMM (Tratamiento de la Información Multilingüe y
Multimodal), Red temática TIMM (Tratamiento de Información Multilingüe y Multimodal), Sevilla, 2014. 
M. J. Castro­Bleda, S. España­Boquera and F. Zamora­Martinez. Encyclopedia of Artificial Intelligence,
chapter Behaviour­based Clustering of Neural Networks, pages 144­151, Information Science Reference,
F. Zamora­Martinez, S. España­Boquera and M. J. Castro­Bleda. Behaviour­based Clustering of Neural
Networks applied to Document Enhancement, in: Computational and Ambient Intelligence, pages 144­151,
Springer, 2007.

Citation Request:

Please refer to the Machine Learning Repository's citation policy [Web Link].

For the database:

F. Zamora-Martinez, S. España-Boquera and M. J. Castro-Bleda, Behaviour-based Clustering of Neural Networks applied to Document Enhancement, in: Computational and Ambient Intelligence, pages 144-151, Springer, 2007.

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