An hybrid MLP-SVM handwritten digit recognizer

Bellili, A.; Gilloux, M.; Gallinari, Patrick

doi:10.1109/icdar.2001.953749

Cited by 27 publications

(16 citation statements)

References 10 publications

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“…SVM, using Gaussian kernels, can perform in this case as well as systems and algorithms designed specifically for this dataset, without including any detailed prior knowledge. [6] combines MLP with SVM for digit recognition. The proposed hybrid architecture is based on the idea that the correct digit class belongs to the two maximum outputs of the MLP, and that SVM can be introduced to detect the correct class among these two classification hypotheses.…”

Section: Svm and Mlp For Automatic Off-linementioning

confidence: 99%

Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition

Frı́as-Martı́nez

Sánchez

Vélez

2006

Engineering Applications of Artificial Intelligence

108

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Abstract. -The problem of automatic signature recognition has received little attention in comparison with the problem of signature verification despite its potential applications for accessing security-sensitive facilities and for processing certain legal and historical documents. This paper presents an efficient off-line human signature recognition system based on Support Vector Machines (SVM) and compares its performance with a traditional classification technique, Multi-Layer Perceptrons (MLP). In both cases we propose two approaches to the problem: (1) construct each feature vector using a set of global geometric and moment-based characteristics from each signature and (2) construct the feature vector using the bitmap of the corresponding signature. We also present a mechanism to capture the intrapersonal variability of each user using just one original signature. Our results empirically show that SVM, which achieves up to 71% correct recognition rate, outperforms MLP.

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Section: Svm and Mlp For Automatic Off-linementioning

confidence: 99%

Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition

Frı́as-Martı́nez

Sánchez

Vélez

2006

Engineering Applications of Artificial Intelligence

108

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“…Nevertheless, they point out that memory space and computational speed for classification still are important issues to be considered when discussing SVMs. In light of this, some authors have proposed using SVMs for verification rather than classification [2]. In such cases, SVMs are used just when the result of the classifier is not so reliable.…”

Section: A Review On Svms For Handwritten Digit Recognitionmentioning

confidence: 98%

Support Vector Machines for Handwritten Numerical String Recognition

Oliveira

Sabourin

Ninth International Workshop on Frontiers in Handwriting Recognition

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In this paper we discuss the use of SVMs to recognize handwritten numerical strings. Such a problem is more complex than recognizing isolated digits since one must deal with problems such as segmentation, overlapping, unknown number of digits, etc. In order to perform our experiments, we have used a segmentation-based recognition system using heuristic over-segmentation. The contribution of this paper is twofold. Firstly, we demonstrate by experimentation that SVMs improve the overall recognition rates. Secondly, we observe that SVMs deal with outliers such as over-and under-segmentation better than multi-layer perceptron neural networks.

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“…There are also similar works using MLP and max margin classifier (like SVMs) in joint. An example of such work can be found in digit recognition [14]. MLP is first used to classify the digits, then SVMs is trained on the classification outputs produced by the MLP.…”

Section: Related Workmentioning

confidence: 99%

Explicit feature mapping via multi-layer perceptron and its application to Mine-Like Objects detection

Shao

Japkowicz

2014

2014 International Joint Conference on Neural Networks (IJCNN)

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In this paper, a novel learning method is introduced that borrows simultaneously from the principles of kernel methods and multi-layer perceptron. Specifically, the method implements the feature mapping idea of kernel methods into a multi-layer perceptron. Unlike in kernel learning where the feature space is usually invisible and inaccessible, the multilayer perceptron based mapping is explicit. Therefore, the proposed model can be learned directly in feature space. Together with the inherent sparse representation, the proposed approach will thus be much faster and easier to train even in the event of a large network size. The proposed approach is applied in the context of an Autonomous Underwater Vehicle Mine-Like Objects detection task. The results show that the proposed approach is able to improve upon the generalization performance of neural network based methods. Its prediction results are also close to or better than those obtained by kernel machines. Its learning and classification speed is shown to far surpass those of kernel machines. These results are confirmed on a number of experiments involving benchmarking UCI domains.

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An hybrid MLP-SVM handwritten digit recognizer

Cited by 27 publications

References 10 publications

Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition

Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition

Support Vector Machines for Handwritten Numerical String Recognition

Explicit feature mapping via multi-layer perceptron and its application to Mine-Like Objects detection

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