Anukriti Bansal

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This paper presents a new approach to detect tabular structures present in document images and in low resolution video images. The algorithm for table detection is based on identifying the unique table start pattern and table trailer pattern. We have formulated perceptual attributes to characterize the patterns. The performance of our table detection system(More)
—This paper presents a novel learning based framework to extract articles from newspaper images using a Fixed-Point Model. The input to the system comprises blocks of text and graphics, obtained using standard image processing techniques. The fixed point model uses contextual information and features of each block to learn the layout of newspaper images and(More)
Layouts and sub-layouts constitute an important clue while searching a document on the basis of its structure, or when textual content is unknown/irrelevant. A sub-layout specifies the arrangement of document entities within a smaller portion of the document. We propose an efficient graph-based matching algorithm, integrated with hash-based indexing, to(More)
The paper presents a novel learning-based framework to identify tables from scanned document images. The approach is designed as a structured labeling problem, which learns the layout of the document and labels its various entities as table header, table trailer, table cell and non-table region. We develop features which encode the foreground block(More)
Digitization of newspaper article is important for registering historical events. Layout analysis of Indian newspaper is a challenging task due to the presence of different font size, font styles and random placement of text and non-text regions. In this paper we propose a novel framework for learning optimal parameters for text graphic separation in the(More)
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