Publication

Comparison of Support Vector Machine and Neural Network for Text Texture Verification

2002
Reports

Abstract

In this paper we propose a method for classifying regions of images and videos frames into text and non-text regions using support vector machine (SVM). Different features are proposed to characterise the texture formed by text characters and background. SVM has an advantage that it is insensitive to the relative numbers of training examples in positive and negative classes. This advantage is are illustrated by comparing results with those obtained using a multiple layer perceptrons (MLP).

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2002
Reports

Abstract

Official source

Related concepts (32)

Support vector machine

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974).

Multilayer perceptron

A multilayer perceptron (MLP) is a misnomer for a modern feedforward artificial neural network, consisting of fully connected neurons with a nonlinear kind of activation function, organized in at least three layers, notable for being able to distinguish data that is not linearly separable. It is a misnomer because the original perceptron used a Heaviside step function, instead of a nonlinear kind of activation function (used by modern networks).

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Related publications (32)

Comparison of Support Vector Machine and Neural Network for Text Texture Verification

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