Offline MODI Character Recognition Using Complex Moments

Sadanand, A. Kulkarni; Borde, Prashant; Manza, Ramesh R.; Pravin, L. Yannawar

doi:10.1016/j.procs.2015.08.067

Cited by 16 publications

(2 citation statements)

References 13 publications

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“…A. Kulkarni Prashant et al (29) , this study attempted to express the effectiveness of Zernike Complex moments and Zernike moments with different zoning patterns for recognizing handwritten MODI characters offline. Each character was broken up into six zoning patterns that had 37 zones.…”

Section: Literature Surveymentioning

confidence: 99%

Review of Character Recognition Techniques for MODI Script

Varpe¹,

Sakhare²

2023

IJST

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Objectives:The main objective of this study is to review and compare the various methods used for Modi script recognition. Methods: The author has chosen various methods from 2010 to 2022 that are used to process MODI Script. The distinct methods employed for feature extraction and classification are compared for various datasets. A discussion on the significance of the selection of correct feature extraction and classification techniques and the comments on the methods suited to specific applications is provided. Findings: Currently, there are very few MODI translators. In contrast, millions of historical documents written in MODI remain unexplored. Novelty: The Convolutional Neural Networks (CNNs) has been used successfully for recognizing MODI Script characters. In the present study the author finds that, compared to all techniques, CNN provides maximum accuracy of 99.78%. Hence, CNN is the best character recognition technique for the MODI script.

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Section: Literature Surveymentioning

confidence: 99%

Review of Character Recognition Techniques for MODI Script

Varpe¹,

Sakhare²

2023

IJST

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“…The 3D feature alongside the proposed two level change based strategy gets better acknowledgment precision. Sadanand et al [14] depicted proficiency of Zernike Complex minutes and Zernike minutes with various Zoning designs for disconnected acknowledgment of manually written 'MODI' characters. Each character was separated in six zoning designs with 37 zones.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Feature Extraction and Multiclass SVM Based Ancient Character Recognition

Balasubrmanie*¹,

Vellingiriraj²

2019

IJRTE

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Ancient character recognition is the most difficult task due to their different formats and less frequent knowledge about the ancient characters. This is performed in the previous research work namely Shape and Size aware Character Recognition and Conversion System (SSCR-CS). This research work proved better character recognition outcome. However this research work might be reduced in performance with lesser detailed information in the images. This is resolved in the proposed research work by introducing the most recent techniques for the character recognition outcome. This is attained by introducing the method namely Hybrid Feature Extraction and Multiclass SVM based recognition method (HFE-MCSVM). In this research work, initially image preprocessing is performed by using Gabor filter. After preprocessing segmentation of characters is performed by using overlapped character segmentation method. After segmentation character recognition is done by introducing the method namely hybrid feature extraction with Multiclass SVM classification approach. The overall assessment of the research work is done in the matlab simulation environment and then it is proved the proposed HFE-MCSVM shows better performance.

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2016

2D and 3D Image Analysis by Moments

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Offline MODI Character Recognition Using Complex Moments

Cited by 16 publications

References 13 publications

Review of Character Recognition Techniques for MODI Script

Review of Character Recognition Techniques for MODI Script

Hybrid Feature Extraction and Multiclass SVM Based Ancient Character Recognition

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