Robust vision-based features and classification schemes for off-line handwritten digit recognition

Teow, Loo-Nin; Loe, Kia-Fock

doi:10.1016/s0031-3203(01)00228-x

Cited by 69 publications

(34 citation statements)

References 18 publications

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“…Their experimental results shows, single SVM classifier is more efficient over rule-based reasoning in recognizing handwritten digits. Teow et al [23] have proposed a digit recognition system that uses a linear SVM classifier by extracting features that are biologically plausible, linearly separable and semantically clear.…”

Section: B Handwritten Character Recognitionmentioning

confidence: 99%

“…It condenses the information from the training data that makes classification much faster than the traditional empirical risk minimization techniques. Because of its generalization capability even in higher dimension, SVM is used in many pattern recognition areas, such as object detection [6], [19][20][21][22], handwritten character recognition [5], [7], [23], face recognition [8], [12], [15] and speech recognition [2], [9], [10], [13], [24]. In this paper, we have briefly summarized about SVM and presented a brief overview of some of the application of SVM in patter recognition.…”

Section: Introduction Support Vector Machine (Svm) Is a Classificamentioning

confidence: 99%

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Review: Support Vector Machines in Pattern Recognition

Borah¹,

Gupta²

2017

IJET

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Abstract-SVM is extensively used in pattern recognition because of its capability to classify future unseen data and its' good generalization performance. Several algorithms and models have been proposed for pattern recognition that uses SVM for classification. These models proved the efficiency of SVM in pattern recognition. Researchers have compared their results for SVM with other traditional empirical risk minimization techniques, such as Artificial Neural Network, Decision tree, etc. Comparison results show that SVM is superior to these techniques. Also, different variants of SVM are developed for enhancing the performance. In this paper, SVM is briefed and some of the pattern recognition applications of SVM are surveyed and briefly summarized.

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Section: B Handwritten Character Recognitionmentioning

confidence: 99%

Section: Introduction Support Vector Machine (Svm) Is a Classificamentioning

confidence: 99%

Review: Support Vector Machines in Pattern Recognition

Borah¹,

Gupta²

2017

IJET

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“…As mentioned in (Teow and Loe, 2002), a common way of reducing the amount of variation is by deslanting the individual digit images. The deslanting method described in (Teow and Loe, 2002) was also adopted in this work and it is briefly described next. For each image, the least-squares regression line passing through the center of mass of the pixels is computed in the first step.…”

Section: Deslanting Digitsmentioning

confidence: 99%

Patch Autocorrelation Features: a translation and rotation invariant approach for image classification

Ionescu

Mothe

et al. 2016

Artif Intell Rev

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“…Among popular handwritten digit databases, the MNIST database has been widely used in recent years as a benchmark for testing new feature extraction and selection methods or for evaluating new classifiers [1][2][3][4][5][6][7][8][9][10]. In the literature, the best accuracies have been achieved for the MNIST database, such as 99.41 % for the robust visionbased features and classification schemes [3], 99.58 % for SVM with gradient features [1], 99.81 % for the convolutional neural networks and elastic distortion [4].…”

Section: Introductionmentioning

confidence: 99%

“…In many studies, more than 100 features have been used to evaluate handwritten digit accuracies for the MNIST database [5][6][7][8]. In certain circumstances, the number of features per character can exceed the number of pixels in the character image [3,9,10]. This increases the processing time of the character recognition.…”

Section: Introductionmentioning

confidence: 99%

Use of wavelet-based two-dimensional scaling moments and structural features in cascade neuro-fuzzy classifiers for handwritten digit recognition

Çetişli

Edizkan

2014

Neural Comput & Applic

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In this paper, a novel handwritten digit recognition system is proposed. The system consist of feature extraction, feature selection and classification stages. The features of digits are extracted by using the moment-based and structural-based methods. For the moment-based method, wavelet-based two-dimensional scaling moments (2-DSMs), which have uniquely different angular divisions of polar form, are considered. The structural-based features including profiles, intersections of horizontal and vertical straight lines, concavity, number and location of holes are used. In the feature selection stage, Fisher's linear discriminant analysis is used to obtain the discriminative features. The feature selection is performed to improve not only the processing time but also recognition rates. In the classification stage, the digits are classified by neuro-fuzzy classifiers (NFCs). A three-stage cascade NFCs with rejection strategy is used in the system to improve the misclassification rate for the handwritten digit recognition task. The experiments are performed on the MNIST and USPS handwritten digit databases. The high correct classification rates of 98.72 % for MNIST and 97.21 % for USPS are attained by using only one hundred robust hybrid features and cascade NFCs. The experiments showed that the proposed system yields better results among those systems that use only moment-based features.

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Robust vision-based features and classification schemes for off-line handwritten digit recognition

Cited by 69 publications

References 18 publications

Review: Support Vector Machines in Pattern Recognition

Review: Support Vector Machines in Pattern Recognition

Patch Autocorrelation Features: a translation and rotation invariant approach for image classification

Use of wavelet-based two-dimensional scaling moments and structural features in cascade neuro-fuzzy classifiers for handwritten digit recognition

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