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Peng X.,State University of New York at Buffalo | Setlur S.,State University of New York at Buffalo | Govindaraju V.,State University of New York at Buffalo | Sitaram R.,HP Labs India
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2011

Document binarization is one of the initial and critical steps for many document analysis systems. Nowadays, with the success and popularity of hand-held devices, large efforts are motivated to convert documents into digital format by using hand-held cameras. In this paper, we propose a Bayesian based maximum a posteriori (MAP) estimation algorithm to binarize the camera-captured document images. A novel adaptive segmentation surface estimation and normalization method is proposed as the preprocessing step in our work and followed by a Markov Random Field based refine procedure to remove noises and smooth binarized result. Experimental results show that our method has better performance than other algorithms on bad or uneven illumination document images. © 2011 SPIE-IS&T. Source


Peng X.,State University of New York at Buffalo | Setlur S.,State University of New York at Buffalo | Govindaraju V.,State University of New York at Buffalo | Ramachandrula S.,HP Labs India
Pattern Recognition Letters | Year: 2012

A boosted tree classifier is proposed to segment machine printed, handwritten and overlapping text from documents with handwritten annotations. Each node of the tree-structured classifier is a binary weak learner. Unlike a standard decision tree (DT) which only considers a subset of training data at each node and is susceptible to over-fitting, we boost the tree using all available training data at each node with different weights. The proposed method is evaluated on a set of machine-printed documents which have been annotated by multiple writers in an office/collaborative environment. The experimental results show that the proposed algorithm outperforms other methods on an imbalanced data set. © 2011 Elsevier B.V. All rights reserved. Source


Audhya G.K.,BSNL Kolkata | Sinha K.,HP Labs India | Mandal K.,University of Waterloo | Dattagupta R.,Jadavpur University | And 2 more authors.
IEEE Transactions on Mobile Computing | Year: 2013

This paper presents a novel method for solving channel assignment problems (CAPs) in hexagonal cellular networks with nonhomogeneous demands in a 2-band buffering system (where channel interference does not extend beyond two cells). The CAP with nonhomogeneous demand is first partitioned into a sequence of smaller subproblems, each of which has a homogeneous demand from a subset of the nodes of the original network. Solution to such a subproblem constitutes an assignment phase, where multiple homogeneous demands are assigned to the nodes corresponding to the subproblem, satisfying all the frequency separation constraints. The whole assignment process for the original network consists of a succession of multiple homogeneous assignments for all the subproblems. Based on this concept, we present a polynomial time approximation algorithm for solving the CAP for cellular networks having nonhomogeneous demands. Our proposed assignment algorithm, when executed on well-known benchmark instances, comes up with an assignment which is always within about 6 percent more than the optimal bandwidth, but requires a very small execution time (less than 5 millisecond on a HPxw8400 workstation). The proposed algorithm is very much suitable for real-life situations, where fast channel assignment is of primary importance, tolerating, however, a marginal deviation (6 percent) from the optimal bandwidth. © 2013 IEEE. Source


Mandalapu D.,HP Labs India | Subramanian S.,University of Bristol
ACM International Conference Proceeding Series | Year: 2011

Pressure is a useful medium for interaction as it can be used in different contexts such as for navigating through depth in 3-D, for time-series visualizations, and in zoomable interfaces. We propose pressure based input as an alternative to repetitive multi-touch interactions, such as expanding/ pinching to zoom. While most user interface controls for zooming or scrolling are bidirectional, pressure is primarily a one-way continuous parameter (from zero to positive). Human ability to control pressure from positive to zero is limited but needs to be resolved to make this medium accessible to various interactive tasks. We first carry out an experiment to measure the effectiveness of various pressure control functions for controlling pressure in both directions (from zero to positive and positive to zero). Based on this preliminary knowledge, we compare the performance of a pressure based zooming system with a multitouch expand/pinch gesture based zooming system. Our results show that pressure input is an improvement to multitouch interactions that involve multiple invocations, such as the one presented in this paper. Copyright © 2011 ACM. Source


Peng X.,Entrance | Setlur S.,Entrance | Govindaraju V.,Entrance | Sitaram R.,HP Labs India
International Journal on Document Analysis and Recognition | Year: 2013

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 referred to as the task of "ink separation" to advance the state-of-art in realizing search of hand-annotated documents. We propose a method which contains two main steps-patch level separation and pixel level separation. In the patch level separation step, the entire document is modeled as a Markov Random Field (MRF). Three different classes (machine printed text, handwritten text and overlapped text) are initially identified using G-means based classification followed by a MRF based relabeling procedure. A MRF based classification approach is then used to separate overlapped text into machine printed text and handwritten text using pixel level features forming the second step of the method. Experimental results on a set of machine-printed documents which have been annotated by multiple writers in an office/collaborative environment show that our method is robust and provides good text separation performance. © 2011 Springer-Verlag. Source

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