Reconstruction of Oriya Alphabets Using Zernike Moments

Tripathy, Jyotsnarani

doi:10.5120/1227-1785

Cited by 14 publications

(5 citation statements)

References 10 publications

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“…Zernike moments are used to extracting the features of printed digits in grayscale images [1]. The Zernike moments uniquely describe functions on the unit disk, and can be extended to images.…”

Section: Theory Of Feature Extractionmentioning

confidence: 99%

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Feature Extraction Using Zernike Moments for Symbols

2017

IJAERD

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Shape identification a n d feature e x t r a c t i o n are the main concern of any pattern recognition system. Object parameters are mostly dependent on spatio-temporal relationships among the pixels. However feature extraction is a complex phenomenon which needs to be addressed from the invariance property, irrespective of position and orientation. Zernike moments are used as shape descriptors and identified as rotation invariant due to Orthogonality property. However the computational complexity is high. Script as a basis for evaluation of patterns and cursive nature of script language SYMBOL. The present work is aimed at evaluation of Zernike moments for various patterns of objects that are cursive in nature. Therefore feature extraction of patterns like vowels and consonants in cursive script SYMBOL using Zernike moments is considered in comparison with Hu's seven moments.

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Section: Theory Of Feature Extractionmentioning

confidence: 99%

“…Zernike Moments-Moments are pure statistical measure of pixel distribution around center of gravity of characters and allow capturing global character shapes information[1]. They are designed to capture both global and geometric information about the image.…”

mentioning

confidence: 99%

Feature Extraction Using Zernike Moments for Symbols

2017

IJAERD

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“…Jyotsnarani Tripathy (2010) developed an efficient Optical Character Recognition(OCR) systems for ORIYA [7] for the reconstruction of the basic characters in Oriya text with different font sizes and types by using Hu's seven moments and Zernike moments. The stages involve binary conversion of the image following up with geometric moment, a pure statistical measure of pixel distribution around centre of gravity.…”

Section: Fig -11mentioning

confidence: 99%

A Bidirectional Text Transcription of Braille for Odia, Hindi, Telugu and English via Image Processing on Fpga

.¹

2015

IJRET

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Communication gap establishes an aura of unwillingness of understanding the factor behind. Basically, this is one of the common agenda for the visually challenged people. In order to bridge this gap, a proper platform of learning for both the mass and the visually challenged for any native language is emphasized in this paper through Braille pattern. Braille, a code, well known to visually challenged as their mode of communication is now converted into normal text in Odia, Hindi, Telugu and English languages using Image segmentation as the base criteria with MATLAB as its simulation field so that every person can be able to easily decode the information being conveyed by these people. The algorithm makes the best use of segmentation, histogram analysis, pattern recognition, letter arrays, data base generation with testing in software and dumping in using Spartan 3e FPGA kit. This paper also elaborates on the reverse conversion of native languages and English to Braille making the paper to be more compatible. The processing speed with efficiency and accuracy defines the effective features of this paper as a successful approach in both software and hardware.

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“…Hu (1962) defined seven moment invariants from geometric moments that are invariants to rotation. The seven features are shown in Appendix (6).…”

Section: Hu Momentsmentioning

confidence: 99%

“…, β is a predetermined value, and m 00 is the zero-th order moment of the original image, which is the object's area in a binary image. An example of Zernike moment application is seen in Tripathy [6]. He developed an optical character recognition (OCR) system by first focusing only on reconstruction of the Indian Oriya alphabet using Zernike moments.…”

Section:  mentioning

confidence: 99%

Comparative Study of Hu Moments and Zernike Moments in Object Recognition

Sabhara¹,

Lee²,

Lim³

2013

SmartCR

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There are lots of ways to perform object recognition. This paper is part of a project studying object recognition. The project is intended as a starting point to further learning about object recognition. Therefore, moment invariants are studied as a good starting point. Hu moment invariant methods and Zernike moment invariant methods are implemented and compared. Zernike moment invariants are shown to outperform Hu moment invariants.

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Reconstruction of Oriya Alphabets Using Zernike Moments

Cited by 14 publications

References 10 publications

Feature Extraction Using Zernike Moments for Symbols

Feature Extraction Using Zernike Moments for Symbols

A Bidirectional Text Transcription of Braille for Odia, Hindi, Telugu and English via Image Processing on Fpga

Comparative Study of Hu Moments and Zernike Moments in Object Recognition

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