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办公室噪声语料数据集

办公室噪声语料数据集

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Computer Classification

Data Set Information:AIMS AND PURPOSESThis corpus is intended to do cleaning (or binarization) and enhancement of noisy......

数据结构 ? 425.95M

    Data Structure ?

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    README.md

    Data Set 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


    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.?Adelantado-Torres,?J.?Pastor-Pellicer,?and?M.?J.?Castro-Bleda.?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.?Castro-Bleda,?S.?Espa?a-Boquera?and?F.?Zamora-Martinez.?Encyclopedia?of?Artificial?Intelligence,
    chapter?Behaviour-based?Clustering?of?Neural?Networks,?pages?144-151,?Information?Science?Reference,
    2009.?
    F.?Zamora-Martinez,?S.?Espa?a-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.


    Citation Request:

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

    For the database:

    F. Zamora-Martinez, S. Espa?a-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.


    M.J. Castro-Bleda (1), S. Espa?a-Boquera (1), J. Pastor-Pellicer (1), F. Zamora-Martinez (2)
    mcastro '@' dsic.upv.es, sespana '@' dsic.upv.es, jpastor '@' dsic.upv.es, francisco.zamora '@' uch.ceu.es

    (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

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