Open AccessA method for handwritten word spotting based on particle swarm optimisation and multi‐layer perceptron
Author(s) -
Tavoli Reza,
Keyvanpour Mohammadreza
Publication year - 2018
Publication title -
iet software
Language(s) - English
Resource type - Journals
ISSN - 1751-8814
DOI - 10.1049/iet-sen.2017.0071
Subject(s) - artificial neural network , computer science , spotting , perceptron , particle swarm optimization , margin (machine learning) , artificial intelligence , classifier (uml) , pattern recognition (psychology) , word (group theory) , layer (electronics) , multilayer perceptron , time delay neural network , coding (social sciences) , fitness function , machine learning , genetic algorithm , mathematics , statistics , chemistry , geometry , organic chemistry
This study presents a new method for handwritten keyword spotting. The innovation in this paper is to provide a model based on neural network architecture and an output based on the margin. At first, a neural network is designed such that its output determines whether a test word as an input is spotted or rejected. The intended neural network has one input layer, two middle layers, and one output layer. Another innovation in this study is optimising neural network weights based on swarm optimisation method. This optimisation model is used to train the neural network, so that the output has adequate margin for classification. The new components of the proposed classifier include new particle coding and new fitness function. Two layers are considered in coding particle, one for activating and deactivating neural network nodes and the other layer for acquiring proper values for weights. Different experiments with variety of parameters were designed for the multi‐layer perceptron neural network. The experiments on three datasets: AMA Arabic dataset, IAM English dataset, and IFN/Farsi dataset yielded 83, 77, and 69% values, respectively, in the best condition. The results demonstrate that the proposed method has been better than the previous ones.