Multiple Features Extraction and Classifiers Combination Based Handwriting Digit Recognition

Khedidja, Derdour; Mouss, Hayet; Bensaadi, Rafik

doi:10.15676/ijeei.2021.13.1.9

Cited by 4 publications

(3 citation statements)

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“…The authors in [15] propose different feature extraction methods to be applied to the MNIST dataset before considering which classifiers to use. They tried four feature extraction methods: Cavity, Hu moments, Zernike Moments, and Hog transformed and combined them with different classifiers.…”

Section: Related Workmentioning

confidence: 99%

MLHandwrittenRecognition: Handwritten Digit Recognition using Machine Learning Algorithms

AbdElminaam

Essam

Samy

et al. 2023

Journal of Computing and Communication

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Handwritten digit recognition has remained a topic of interest to computer vision scientists. Its origination precedes the emergence of the machine as it is a crucial component of the digital transformation of the majority of institutions in numerous fields. With the uprising of machine models, choosing a satisfactory and fit algorithm for this multi-class (0-9) classification problem became challenging. This paper aims to compare seven machine learning algorithms in terms of their performance metrics in recognizing handwritten digits employing two datasets. The -Nearest Neighbors (kNN), Support Vector Machine (SVM), Logistic Regression, Neural Network, Random Forest (RF), Naive Bayes, and Decision Tree models are accordingly evaluated concerning the Area Under the Curve (AUC), accuracy (ACC), F1-score (F1), precision (PREC), and recall (REC). The widely used Modified National Institute of Standards and Technology database (MNIST) dataset and the Handwritten Digit Classification dataset (HDC) have been the providers of the images on which this research is conducted. The results confirm that the Neural Networks model is a great classifier for this problem; however, it presents similar results to other machine learning classifiers in several cases. Therefore, this paper does not provide an absolute choice of a classifier for the handwritten digit recognition problem but rather explains the reason behind the performance of each model.

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Section: Related Workmentioning

confidence: 99%

MLHandwrittenRecognition: Handwritten Digit Recognition using Machine Learning Algorithms

AbdElminaam

Essam

Samy

et al. 2023

Journal of Computing and Communication

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“…Subsequently, these extracted features were applied to machine learning algorithms, i.e., AdaBoost, K-nearest neighbors (kNNs), linear discriminant analysis, multilayer perceptron (MLP), radial basis function network, random forest, and support vector machine (SVM), and the results showed a classification performance. Derdour et al [16] proposed a system for recognizing handwritten digits using a combination of different invariant feature extraction methods and multiple classifiers; in particular, cavities, Zernike moments, Hu moments, and the histogram of gradient were used for feature extraction. This study aimed to improve the accuracy of recognizing handwritten digits in the modified National Institute of Standards and Technology database using a Tree, a kNN, an MLP, an SVMOVO, and an SVMOVA.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we focused on the basic components of images through relatively simple and easy feature analysis instead of using multiple detectors or performing large-scale models and complex processing as in previous studies [7][8][9][10][11][12][13][14][15][16][17]. These components are essential features used in the diagnosis of skin lesions, and we aimed to extract them to reduce the scale and complexity of neural models.…”

Section: Introductionmentioning

confidence: 99%

Skin Lesion Classification Using Hybrid Convolutional Neural Network with Edge, Color, and Texture Information

et al. 2023

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Herein, a new paradigm based on deep learning was proposed that allows the extraction of fine-grained differences between skin lesions in pixel units for high accuracy classification of skin lesions. As basic feature information for a dermoscopic image of a skin region, 50 different features were extracted based on the edge, color, and texture features of the skin lesion image. For the edge features, a line-segment-type analysis algorithm was used, wherein the visual information of a dermoscopic image was precisely analyzed in terms of the units of pixels and was transformed into a structured pattern. Regarding the color features of skin lesions, the dermoscopic image was transformed into multiple color models, and the features were acquired by analyzing histograms showing information regarding the distribution of pixel intensities. Subsequently, texture features were extracted by applying the well-known Law’s texture energy measure algorithm. Feature data (50 × 256) generated via the feature extraction process above were used to classify skin lesions via a one-dimensional (1D) convolution layer-based classification model. Because the architecture of the designed model comprises parallel 1D convolution layers, fine-grained features of the dermoscopic image can be identified using different parameters. To evaluate the performance of the proposed method, datasets from the 2017 and 2018 International Skin Imaging Collaboration were used. A comparison of results yielded by well-known classification models and other models reported in the literature show the superiority of the proposed model. Additionally, the proposed method achieves an accuracy exceeding 88%.

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Handwritten Digit Classification based on Ensemble Majority Voting Classifier

Alsharif,

Abdo,

Bozed

2023

2023 IEEE 11th International Conference on Systems and Control (ICSC)

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Multiple Features Extraction and Classifiers Combination Based Handwriting Digit Recognition

Cited by 4 publications

References 0 publications

MLHandwrittenRecognition: Handwritten Digit Recognition using Machine Learning Algorithms

MLHandwrittenRecognition: Handwritten Digit Recognition using Machine Learning Algorithms

Skin Lesion Classification Using Hybrid Convolutional Neural Network with Edge, Color, and Texture Information

Handwritten Digit Classification based on Ensemble Majority Voting Classifier

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