An efficient character recognition method using enhanced HOG for spam image detection

Naiemi, Fatemeh; Ghods, Vahid; Khalesi, Hassan

doi:10.1007/s00500-018-03728-z

Cited by 28 publications

(14 citation statements)

References 24 publications

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“…A number of researchers have used CHARS74k dataset for recognition of Kannada script. Naiemi [78] applied histogram of oriented gradients features on CHARS74k dataset for spam image detection, while [113] used the dataset for recognizing characters in early Indian printed documents. Joe et, al [114] used CNN to recognize offline handwritten characters written in Kannada script.…”

Section: B Chars74kmentioning

confidence: 99%

“…These features claim to provide character recognition accuracy of 98.8% and 99.6%. During a study on Kannada, researchers used the HOG feature extraction method along with SVM classifier for spam image detection with an accuracy of 94.2% [78]. Research on Indian scripts is very diverse, and a number of researchers are involved in research on multiple scripts.…”

Section: F Indian Scriptmentioning

confidence: 99%

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Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Memon

Sami

Khan³

et al. 2020

IEEE Access

310

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Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence / machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2019. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 176 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.

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Section: B Chars74kmentioning

confidence: 99%

Section: F Indian Scriptmentioning

confidence: 99%

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Memon

Sami

Khan³

et al. 2020

IEEE Access

310

View full text Add to dashboard Cite

show abstract

“…In addition, as the degree of lesions increases, the frequency of the gray value distribution in the 50-235 region increases, and the difference in the gray distribution between normal and cervical cancer is the most obvious. 2) HOG FEATURES HOG features have strong image structure and contour description capabilities as well as a strong recognition effect on the description of local areas [19], [20]. HOG features are also suitable for describing texture features.…”

Section: ) Gray Statisticsmentioning

confidence: 99%

Classification of Cervical Biopsy Images Based on LASSO and EL-SVM

Huang

Zhang

et al. 2020

IEEE Access

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Cervical biopsy (biopsy) is an important part of the diagnosis of cervical cancer. The artificial classification of biopsy images in diagnosis is difficult and depends on the clinical experience of pathologists. However, the classification accuracy of computerized biopsy tissue images with similar lesions is low, and the problem of incomplete experimental objects needs to be addressed. This paper proposes a method of cervical biopsy tissue image classification based on least absolute shrinkage and selection operator (LASSO) and ensemble learning-support vector machine (EL-SVM). Using the LASSO algorithm for feature selection, the average optimization time was reduced by 35.87 seconds while ensuring the accuracy of the classification, and then serial fusion was performed. The EL-SVM classifier was used to identify and classify 468 biopsy tissue images, and the receiver operating characteristic (ROC) curve and error curve were used to evaluate the generalization ability of the classifier. Experiments show that the normalcervical cancer classification accuracy reached 99.64%, the normal-low-grade squamous intraepithelial lesion (LSIL) classification accuracy was 84.25%, the normal-high-grade squamous intraepithelial lesion (HSIL) classification accuracy was 87.40%, the LSIL-HSIL classification accuracy was 76.34%, the LSILcervical cancer classification accuracy was 91.88%, and the HSIL-cervical cancer classification accuracy was 81.54%.

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“…Text-based 9 [18], [19], [20], [21], [22], [23], [24], [25], [26] Image-based 16 [27], [28], [29], [9], [30], [31], [32], [33], [34], [35], [17], [5], [36], [37], [38], [39] [20], [30], [34], [5], [37], [38], [39] Dredze 10 5789 spam (spam =3239 & ham = 2550) [30], [31], [33], [34], [40], [17], [5], [37], [28], [21] Enron corpus 1 Not specified [22] SMS spam 1 Not specified [18] Princeton spam corpus [20], [24], [30], [31], [41], [32],…”

Section: No Of Studies Referencementioning

confidence: 99%

“…Image spam is a concept that began in early 2005. More than 50% of the spam was made up of images by the end of 2006 [9], [6]. Image spam is another modern challenge in a phishing email.…”

Section: Introductionmentioning

confidence: 99%