Recently, the mixed raster content model was proposed for compound document image compression. Most state-of-the-art document image compression methods, such as DjVu, work on the basis of this model but they have some disadvantages, especially for Farsi and Arabic document images. First, the Farsi/Arabic script has some characteristics which can be used to further improve the compression performance. Second, existing segmentation methods have focused on well-separating the textual objects from the background and/or optimizing the rate-distortion trade-off; nevertheless, they have not considered the text readability and OCR facility. Third, these methods usually suffer from the undesired jaggy artifact and misclassifying the important textual details. In this paper, MRC-based document image compression method is proposed which compromises rate-distortion trade-off better than the existing state-of-the-art document compression methods. The proposed method has higher performance in the aspects of segmentation, bi-level mask layer compression, OCR facility, and the overall compression. It uses a 1D pattern matching technique for compression of masklayer. It also uses a segmentation method which is sensitive enough to the small textual objects. Experimental results show that the proposed method has considerably higher compression performance than that of the state-of-the-art compression method DjVu, as high as 1.75-2.3.

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{{Explor lien
   |wiki=    Ticri/CIDE
   |area=    OcrV1
   |flux=    Main
   |étape=   Exploration
   |type=    RBID
   |clé=     Pascal:10-0182404
   |texte=   Farsi and Arabic document images lossy compression based on the mixed raster content model
}}