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Extraction of text areas is a necessary first step for taking a complex document image for diameter recognition task. In digital libraries, such OCR'ed text facilitates access to the image of document page through keyword search. Gabor filters, known to be simulating certain characteristics of the human visual system (HVS), have been employed for this task by a large number of scientists, in scanned document images. Adapting such a scheme for camera based document images is a relatively new approach. Moreover, design of the appropriate filters to separate text areas, which are assumed to be rich in high frequency components, from nontext areas is a difficult task. The difficulty increases if the clutter is also rich in high frequency components. Other reported works, on separating text from nontext areas, have used geometrical/structural information like shape and size of the regions in binarized document images. In this work, we have used a combination of the above mentioned approaches for the purpose. We have used connected component analysis (CCA), in binarized images, to segment nontext areas based on the size information of the connected regions. A Gabor function based filter bank is used to separate the text and the nontext areas of comparable size. The technique is shown to work efficiently on different kinds of scanned document images, camera captured document images and sometimes on scenic images.