Novel features and a cascaded classifier based Arabic numerals recognition system

Prasad, Binod Kumar; Sanyal, Goutam

doi:10.1007/s11045-016-0466-4

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…Xu et al [33] presented a nonnegative representation-based classifier for pattern classification, which achieved accuracies of 99% and 95.1% on the MNIST and USPS datasets, respectively. Prasad et al [34] presented novel features and cascaded classifiers KNN and SVM, resulting in an accuracy of 99.26 on the MNIST dataset.…”

Section: State Of the Artmentioning

confidence: 99%

Two-Stage Feature Generator for Handwritten Digit Classification

Gunler Pirim,

Tora,

Oztoprak

et al. 2023

Sensors

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In this paper, a novel feature generator framework is proposed for handwritten digit classification. The proposed framework includes a two-stage cascaded feature generator. The first stage is based on principal component analysis (PCA), which generates projected data on principal components as features. The second one is constructed by a partially trained neural network (PTNN), which uses projected data as inputs and generates hidden layer outputs as features. The features obtained from the PCA and PTNN-based feature generator are tested on the MNIST and USPS datasets designed for handwritten digit sets. Minimum distance classifier (MDC) and support vector machine (SVM) methods are exploited as classifiers for the obtained features in association with this framework. The performance evaluation results show that the proposed framework outperforms the state-of-the-art techniques and achieves accuracies of 99.9815% and 99.9863% on the MNIST and USPS datasets, respectively. The results also show that the proposed framework achieves almost perfect accuracies, even with significantly small training data sizes.

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Section: State Of the Artmentioning

confidence: 99%

Two-Stage Feature Generator for Handwritten Digit Classification

Gunler Pirim,

Tora,

Oztoprak

et al. 2023

Sensors

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“…Much research has been done on printed and handwritten digit recognition using artificial neural network: [10], [12], [13], [18]- [28]. Recently, researchers have begun to pay attention to improve the performance by integration of multiple classifier such as [1], [9], [29]- [37].…”

Section: Related Workmentioning

confidence: 99%

Multiple Classifiers and Invariant Features Extraction for Digit Recognition

Khedidja¹,

Mouss²

2019

IJCEE

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Extraction of feature and classification methods are important phases in recognition system. A good classifier and extraction of features that suits play a very important role in a recognition system to improving recognition rate. In this paper we propose a new system designed to recognize the ten digits of printed Arabic numerals that are the most common symbolic representation of numbers in the world today. This study has been conducted using Hu moment, number of hole and surface which are tolerate to the geometric transformations along with seven different classifiers Naive Bayesian, Multi-Layer Perceptron (MLP), Linear Discriminant Analysis (LDA), Pseudo-Inverse. Support Vector Machine (SVM), Decision Tree and K-Nearest Neighbor (KNN). Classifier combination is considered. Experimental tests demonstrated that our technique achieves good results on multi-font and multi-size printed digit dataset.

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“…SVM serves to create a classification hyperplane as the decision surface; this hyper-plane separates positive from negative samples and maximizes the isolation edge between them [6]. SVM has not only enriched statistical theory itself, but also allowed for advancements in text categorization [7]- [9], image analysis [10]- [12], handwriting recognition [13], [14], face recognition [15]- [17], fault diagnosis [18], [19], biological sciences [20]- [22], and other applications.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Model Based on TVIW-PSO-GSA Algorithm and Support Vector Machine for Classification Problems

Xue

Bai

et al. 2019

IEEE Access

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The increasingly serious haze problem in China has brought about a growing public awareness of air quality. Precise air quality index (AQI) forecasts play an important role in both controlling air pollution and promoting the sustainable development of human society. However, the randomness, non-stationarity, and irregularity of the AQI series make its classifications very difficult. This paper introduces a time-varying inertia weighting (TVIW) strategy based on a combination of gravitation search algorithm (GSA) and particle swarm optimization (PSO) called the TVIW-PSO-GSA. The TVIW-PSO-GSA is utilized to optimize the penalty parameter C and kernel function parameter γ of a support vector machine (SVM) to create a hybrid TVIW-PSO-GSA-SVM algorithm. Twenty-three benchmark functions, five UCI datasets, and an AQI hierarchical classification example are tested to find that the convergence speed and performance of the TVI-PSO-GSA exceed those of other algorithms, and the TVIW-PSO-GSA-SVM algorithm also achieves higher classification accuracy and efficiency than the PSO-GSA-SVM, GSA-SVM, GA-SVM, or PSO-SVM, which indicates that the TVIW-PSO-GSA-SVM reliably and accurately classifies AQI and UCI datasets.INDEX TERMS Intelligent optimization algorithm, GSA, PSO, time-varying inertia weighting strategy, SVM.

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Novel features and a cascaded classifier based Arabic numerals recognition system

Cited by 6 publications

References 20 publications

Two-Stage Feature Generator for Handwritten Digit Classification

Two-Stage Feature Generator for Handwritten Digit Classification

Multiple Classifiers and Invariant Features Extraction for Digit Recognition

A Novel Hybrid Model Based on TVIW-PSO-GSA Algorithm and Support Vector Machine for Classification Problems

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