Text-Image Segmentation and Compression using Adaptive Statistical Block Based Approach

Nidhal Kamel Taha El-Omari, Ahmad H. Al-Omari, Ali Mohammad H. Al-Ibrahim, Tariq Alwada'n

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    Images and scanned text documents are gradually
    more used in a vast range of applications. To reduce the needed
    storage or to accelerate their move through the computers
    networks, the document images have to be compressed.
    Traditional compression mechanisms, which are generally
    developed with a particular image type and purpose, are facing
    many challenges with mixed documents. This paper describes a
    statistical block-based technique for an automatic document
    image segmentation and compression. Based on the number of
    detected colors in each region of the image, this approach creates
    a new representation of the image that can produce very
    highly-compressed document files that nonetheless retain
    excellent image quality. The proposed algorithm segments the
    compound document image into blocks of equal size. The blocks
    are classified into seven different categories. Each category
    represents an image part that shares the same properties. A new
    representation of each category is formed and the similar adjacent
    blocks are merged to form labeled regions sharing the same
    properties. At the end, to achieve better compression ratio, the
    different regions of the image are compressed using different
    compression techniques.
    Original languageEnglish
    Number of pages9
    JournalInternational Journal of Engineering and Advanced Technology
    Issue number4
    Publication statusPublished - 30 Apr 2017


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