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Proceedings ArticleDOI

Most Discriminative Primitive Selection for Identity Determination Using Handwritten Devanagari Script

TL;DR: An approach for selecting best discriminative primitives for writer recognition is presented and a hybrid system by combining both writer recognition and handwriting recognition for improved accuracy is proposed.
Abstract: Writer recognition based on peculiarity of hand-writing is an important aspect of any forensic analysis. We present an approach for selecting best discriminative primitives for writer recognition. After selecting the primitives we also propose a hybrid system by combining both writer recognition and handwriting recognition for improved accuracy. We have also validated the performance of selected primitives on publically available dataset. We have performed this study on the Devanagri script. Experimental results verified the effectiveness of the proposed franework.
Citations
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Journal ArticleDOI
TL;DR: Investigational outcome shows that the proposed technique is competent to segment characters from Devanagari offline handwritten scripts, and accuracy of outcomes is up to 99.578 %.
Abstract: In this research work, we have proposed a segmentation technique for words and characters of Devanagari offline handwritten scripts. Due to complex structures and high unevenness in writing styles, recognition of words and characters from the unconstrained scripts has become a burning vicinity of interest for researchers. The proposed Pixel Plot and Trace and Re-plot and Re-trace (PPTRPRT) technique extracts text region from Devanagari offline handwritten scripts and lead iterative processes for segmentation of text lines along with skew and de-skew operations. The outcomes of iterations are used in pixel-space-based word segmentation, and the segmented words are used in segmentation of characters. Moreover, PPTRPRT perform various normalization steps to allow deviation in pen breadth and slant in inscription. Investigational outcome shows that the proposed technique is competent to segment characters from Devanagari offline handwritten scripts, and accuracy of outcomes is up to 99.578 %.

24 citations

Proceedings ArticleDOI
03 Mar 2016
TL;DR: A framework for the selection of a subset of phonemes from the speech signal at the speaker level in order to capture the speaker variability in this selection process is presented and experimental results verified the effectiveness of the proposed framework.
Abstract: Speaker voice characteristics are an important aspect of forensic phonetics. Previous studies have suggested that all the features present in the speech signals are not equally important for speaker discrimination, and it is well-known that subsets of phonemes are more informative than others. However, most of theses studies have concerned a whole group of speakers, without taking into account the speaker specificities. This paper presents a framework for the selection of a subset of phonemes from the speech signal at the speaker level in order to capture the speaker variability in this selection process. We present the approach for the selection of the most discriminatory phonemes and a preliminary study have been performed on French reading speech database. At the global level, the most discriminatory phonemes are compared to previous studies. At the speaker level, we have examined the inter-speaker variability according to their most discriminatory phonemes. The experimental results verified the effectiveness of the proposed framework.

1 citations


Cites background from "Most Discriminative Primitive Selec..."

  • ...The details of the algorithm can be found in [21]....

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Proceedings ArticleDOI
01 Dec 2013
TL;DR: This paper is aimed at exploring the potential of using discriminatory primitives containing words for the task of detecting skilled forgeries, and considers handwritten Devanagri documents for this work.
Abstract: This paper is aimed at exploring the potential of using discriminatory primitives containing words for the task of detecting skilled forgeries We consider handwritten Devanagri documents for this work We have obtained experimental handwriting data from subjects who have contributed handwriting samples in their natural handwriting Other authors are asked to imitate the writing style of the subjects to produce a skilled forgery sample Most of the literature dealing with writer recognition focus on signatures and very few reports have addressed the problem of detecting forgeries for handwritten Indian scripts We also use multiple words based classification for the targeted task of forgery detection Our experiments show encouraging results

1 citations

References
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Journal ArticleDOI

37,017 citations


"Most Discriminative Primitive Selec..." refers methods in this paper

  • ...The binarization is performed by global threshold computed using Otsus method[10]....

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Proceedings ArticleDOI
20 Jun 2005
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Abstract: We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.

31,952 citations


"Most Discriminative Primitive Selec..." refers methods in this paper

  • ...For our primitives, we use Histograms of Oriented Gradient (HOG) [1] features....

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  • ...We extracted HOG features from the primitives....

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  • ...In the experiments, the HOG contains 100 rectangular bins for character image representation....

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  • ...The extracted gradient can be represented in the form of HOG....

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  • ...We extracted HOG features from the segmented primitives and used sequential feature selection algorithm with KNN for the selection of distinct primitives....

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Journal ArticleDOI
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
Abstract: Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. These areas include text processing of internet documents, gene expression array analysis, and combinatorial chemistry. The objective of variable selection is three-fold: improving the prediction performance of the predictors, providing faster and more cost-effective predictors, and providing a better understanding of the underlying process that generated the data. The contributions of this special issue cover a wide range of aspects of such problems: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.

14,509 citations


"Most Discriminative Primitive Selec..." refers background in this paper

  • ...Primitive selection process selects the best combination of primitives wherein goodness of subsets is measured by evaluating some criteria function [5]....

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Journal ArticleDOI
TL;DR: The nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms are described.
Abstract: Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered.

2,653 citations

Journal ArticleDOI
01 Sep 2008
TL;DR: This paper presents the state of the art in automatic signature verification and addresses the most valuable results obtained so far and highlights the most profitable directions of research to date.
Abstract: In recent years, along with the extraordinary diffusion of the Internet and a growing need for personal verification in many daily applications, automatic signature verification is being considered with renewed interest. This paper presents the state of the art in automatic signature verification. It addresses the most valuable results obtained so far and highlights the most profitable directions of research to date. It includes a comprehensive bibliography of more than 300 selected references as an aid for researchers working in the field.

688 citations