Srirangaraj Setlur

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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 con-nectivity 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)
In this paper, we describe an approach to segment handwritten text, machine printed text and noise from annotated machine printed documents. Three categories of word level features are extracted. We use a modified K-Means clustering algorithm for classification followed by a relabeling procedure using Markov Random Field(MRF) based on a concept of(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)
In this paper, we present a novel approach for detecting and removing pre-printed rule-lines from binary handwritten Arabic document images. The proposed technique is based on a directional local profiling approach for the detection of the rule-line locations. Then a refined adaptive vertical run-length search is designed for removing the rule-line pixels(More)
Separating machine printed text and handwriting from overlapping text is a challenging problem in the document analysis field and no reliable algorithms have been developed thus far. In this paper, we propose a novel approach for separating handwriting from binary image of overlapped text. Instead of using fixed size training patches, we describe an(More)