Comparison of Feature Extraction Methods for Recognition of Isolated Handwritten Characters in Gurmukhi Script

Sharma, Dharam Veer; Jhajj, Puneet

doi:10.1007/978-3-642-19403-0_18

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…However, selection of feature types is a major issue in OCR development [92]. Researchers recommend that the feature extraction methods should be independent of scalable font characteristics such as font styles, font types, font sizes and should be able to describe and distinguish different patterns effectively [92], [93]. In other words, a study emphasizes that the key purpose of selecting good features is to maximize the effectiveness and the efficiency of the OCR system minimizing the complexity and processing time simultaneously [94].…”

Section: E Feature Extraction Phasementioning

confidence: 99%

Printed Arabic Script Recognition: A Survey

Alghamdi¹,

Teahan²

2018

ijacsa

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Optical character recognition (OCR) is essential in various real-world applications, such as digitizing learning resources to assist visually impaired people and transforming printed resources into electronic media. However, the development of OCR for printed Arabic script is a challenging task. These challenges are due to the specific characteristics of Arabic script. Therefore, different methods have been proposed for developing Arabic OCR systems, and this paper aims to provide a comprehensive review of these methods. This paper also discusses relevant issues of printed Arabic OCR including the challenges of printed Arabic script and performance evaluation. It concludes with a discussion of the current status of printed Arabic OCR, analyzing the remaining problems in the field of printed Arabic OCR and providing several directions for future research.

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Section: E Feature Extraction Phasementioning

confidence: 99%

Printed Arabic Script Recognition: A Survey

Alghamdi¹,

Teahan²

2018

ijacsa

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A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition

Mohamad¹,

Hassan²,

Nasien³

et al. 2015

ijacsa

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Abstract-The development of handwriting character recognition (HCR) is an interesting area in pattern recognition.HCR system consists of a number of stages which are preprocessing, feature extraction, classification and followed by the actual recognition. It is generally agreed that one of the main factors influencing performance in HCR is the selection of an appropriate set of features for representing input samples. This paper provides a review of these advances. In a HCR, the set of features plays as main issues, as procedure in choosing the relevant feature that yields minimum classification error. To overcome these issues and maximize classification performance, many techniques have been proposed for reducing the dimensionality of the feature space in which data have to be processed. These techniques, generally denoted as feature reduction, may be divided in two main categories, called feature extraction and feature selection. A large number of research papers and reports have already been published on this topic. In this paper we provide an overview of some of the methods and approach of feature extraction and selection. Throughout this paper, we apply the investigation and analyzation of feature extraction and selection approaches in order to obtain the current trend. Throughout this paper also, the review of metaheuristic harmony search algorithm (HSA) has provide.

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Comparison of Feature Extraction Methods for Recognition of Isolated Handwritten Characters in Gurmukhi Script

Cited by 2 publications

References 5 publications

Printed Arabic Script Recognition: A Survey

Printed Arabic Script Recognition: A Survey

A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition

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