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Automatic identification of a script in a given document image facilitates many important applications such as automatic archiving of multilingual documents, searching online archives of document images and for the selection of script specific OCR in a multilingual environment. In this paper, we present a scheme to identify different Indian scripts from a document image. This scheme employs hierarchical classification which uses features consistent with human perception. Such features are extracted from the responses of a multi-channel log-Gabor filter bank, designed at an optimal scale and multiple orientations. In the first stage, the classifier groups the scripts into five major classes using global features. At the next stage, a sub-classification is performed based on script-specific features. All features are extracted globally from a given text block which does not require any complex and reliable segmentation of the document image into lines and characters. Thus the proposed scheme is efficient and can be used for many practical applications which require processing large volumes of data. The scheme has been tested on 10 Indian scripts and found to be robust to skew generated in the process of scanning and relatively insensitive to change in font size. This proposed system achieves an overall classification accuracy of 97.11% on a large testing data set. These results serve to establish the utility of global approach to classification of scripts. [ABSTRACT FROM AUTHOR]

The notion of topographic features like ridges, trenches, hills, etc. is formed by visualising the 2D image function as a surface in 3D space. Hence, properties of such a surface can be used to detect features from images. One such property, the curvature of the image surface, can be used to detect features characterised by a sharp bend in the surface. Curvature based feature detection requires an efficient technique to estimate/calculate the surface curvature. In this paper, we present an alternative measure for curvature and provide an analysis of the same to determine its scope. Feature detection algorithms using this measure are formulated and two applications are chosen to demonstrate their performance. The results show good potential of the proposed measure in terms of efficiency and scope. [ABSTRACT FROM AUTHOR]

Boundary detection in natural images is a fundamental problem in many computer vision tasks. In this paper, we argue that early stages in primary visual cortex provide ample information to address the boundary detection problem. In other words, global visual primitives such as object and region boundaries can be extracted using local features captured by the receptive fields. The anatomy of visual cortex and psychological evidences are studied to identify some of the important underlying computational principles for the boundary detection task. A scheme for boundary detection based on these principles is developed and presented. Results of testing the scheme on a benchmark set of natural images, with associated human marked boundaries, show the performance to be quantitatively competitive with existing computer vision approaches. [ABSTRACT FROM AUTHOR]