Recognition of handwritten devanagiri numerals by graph representation and SVM

Bhat, Mohammad Idrees; Sharada, B.

doi:10.1109/icacci.2016.7732333

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…In cases where a correspondence is obtained through an automatic method, it is usually deduced through an optimisation process called error-tolerant graph matching. Several graph matching methods have been proposed in recent years [1], [2], [3], [4], [5] to address a variety of problems such as general pattern recognition and image processing [6], text interpretation [7], symbol classification in schematic diagrams [8], chemical and protein compound association, biometrics, malware detection in networks [9], 2D to 3D process plant diagram conversion [10], among others. As a result, it is possible to generate more than one correspondence between a single pair of graphs.…”

Section: Introductionmentioning

confidence: 99%

Correspondence edit distance to obtain a set of weighted means of graph correspondences

Moreno‐García

Serratosa

Jiang

2020

Pattern Recognition Letters

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights Edit distance and graph matching Weighted mean and generalized median Definition of a distance between graph correspondences New algorithm to deduce the set of weighted means of correspondences

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Section: Introductionmentioning

confidence: 99%

Correspondence edit distance to obtain a set of weighted means of graph correspondences

Moreno‐García

Serratosa

Jiang

2020

Pattern Recognition Letters

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Spectral Graph-based Features for Recognition of Handwritten Characters: A Case Study on Handwritten Devanagari Numerals

Bhat

Sharada

2018

Journal of Intelligent Systems

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Abstract Interpretation of different writing styles, unconstrained cursiveness and relationship between different primitive parts is an essential and challenging task for recognition of handwritten characters. As feature representation is inadequate, appropriate interpretation/description of handwritten characters seems to be a challenging task. Although existing research in handwritten characters is extensive, it still remains a challenge to get the effective representation of characters in feature space. In this paper, we make an attempt to circumvent these problems by proposing an approach that exploits the robust graph representation and spectral graph embedding concept to characterise and effectively represent handwritten characters, taking into account writing styles, cursiveness and relationships. For corroboration of the efficacy of the proposed method, extensive experiments were carried out on the standard handwritten numeral Computer Vision Pattern Recognition, Unit of Indian Statistical Institute Kolkata dataset. The experimental results demonstrate promising findings, which can be used in future studies.

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Recognition of handwritten devanagiri numerals by graph representation and SVM

Cited by 2 publications

References 13 publications

Correspondence edit distance to obtain a set of weighted means of graph correspondences

Correspondence edit distance to obtain a set of weighted means of graph correspondences

Spectral Graph-based Features for Recognition of Handwritten Characters: A Case Study on Handwritten Devanagari Numerals

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