International Journal of Computational Intelligence Techniques

35-43

Authors: DHORE M.P., THAKARE V.M., KALE K.V

Document Image Analysis facilitates the transformation of any information presented on paper and addressed to human comprehension into an equivalent symbolic representation accessible to any kind of computer information processing. Wide variety of information is now being converted into electronic form for better storage and intelligent processing. This needs processing of documents using image analysis algorithms. The document analysis decomposes a document image into several consistent items which represent coherent components of the documents such as text lines, photographs and graphics, without any knowledge of specific format. This paper uses a morphological approach for segmentation of a document image. The morphological approach quantitatively describes operations effective for the shape of objects in an image. The mathematical morphology describes such operations by combinations of basic set operations between an image and a small object called a structuring element. It efficiently deals with geometrical features such as size, shape, contrast, or connectivity that can be considered as segmentation oriented features. The simplification for segmentation can be efficiently achieved by filters based on opening and closing by partial reconstruction. The size of structuring element is varied to allow the introduction of more local information to improve the segmentation. In this paper, considering the advantages of morphological approach, it is used for segmenting the document images. The proposed technique is robust enough to include various documents like multi-column, multi-article, multi-font, multi-orientation and multi-lingual.

53-55

Authors: MADHUSUDHANA RAO T.V., SETTY S.P

In this paper we present a novel methodology for image classification based on Generalized Gaussian Mixture models together with PCA. In this paper we consider the texture features in the identification of the images. The developed model is evaluated using various quality metrics such as PSNR, and MSE. The bench mark images are considered for experimentation. The output shows that the developed model outperforms the existing models in terms of image retrievals.