Srirangaraj Setlur

Learn More
In this paper, we present a new text line extraction method for handwritten Arabic documents. The proposed technique is based on a generalized adaptive local connectivity map (ALCM) using a steerable directional filter. The algorithm is designed to solve the particularly complex problems seen in handwritten documents such as fluctuating, touching or(More)
This paper presents an algorithm using adaptive local connectivity map for retrieving text lines from the complex handwritten documents such as handwritten historical manuscripts. The algorithm is designed for solving the particularly complex problems seen in handwritten documents. These problems include fluctuating text lines, touching or crossing text(More)
We outline two different techniques for OCR of machine printed, multi-font Devanagari text. In the first design, words are segmented along linear boundaries. Subsequently, classification is performed with the assumption of accurate segmentation. The second approach uses classifiers to obtain preliminary hypothesis for each segment of the word. These results(More)
This paper describes a novel recognition driven segmentation methodology for Devanagari Optical Character Recognition. Prior approaches have used sequential rules to segment characters followed by template matching for classification. Our method uses a graph representation to segment characters. This method allows us to segment horizontally or vertically(More)
In this paper we present a top-down, projection-profile based algorithm to separate text blocks from image blocks in a Devanagari document. We use a distinctive feature of Devanagari text, called Shirorekha (Header Line) to analyze the pattern produced by Devanagari text in the horizontal profile. The horizontal profile corresponding to a text block(More)
The convenience of search, both on the personal computer hard disk as well as on the web, is still limited mainly to machine printed text documents and images because of the poor accuracy of handwriting recognizers. The focus of research in this paper is the segmentation of handwritten text and machine printed text from annotated documents sometimes(More)
In this paper, a novel Markov random fields (MRF) based binarization algorithm is proposed to segment foreground text from document images captured using hand-held devices (such as cell-phone or digital camera). In the MRF based framework, an edge potential feature is extracted to preserve the strokes of foreground text and to remove isolated noise and an(More)
Palm leaves were one of the earliest forms of writing media and their use as writing material in South and Southeast Asia has been recorded from as early as the fifth century B.C. until as recently as the late 19th century. Palm leaf manuscripts relating to art and architecture, mathematics, astronomy, astrology, and medicine dating back several hundreds of(More)