Document zone content classification for technical document images using Artificial Neural Networks and Support Vector Machines

• Main Authors: Ibrahim, Z., Isa, D., Rajkumar, R., Kendall, G.
• Format: Conference Proceeding
• Language: English
• Subjects: Artificial neural networks; Backpropagation algorithms; Backpropagation Neural Network; Image recognition; Multilayer perceptrons; Non-text Classification; Organizing; Pattern recognition; Recurrent Neural Network; Recurrent neural networks; Self Organizing Map; Support Vector Machine; Support vector machine classification; Support vector machines; Text recognition
• DOI: 10.1109/ICADIWT.2009.5273957

Artificial Neural Networks (ANN) are a classic pattern classifier and widely applicable to various problems and are relatively easy to use. Three of the most popular ANNs are Multilayer Perceptron (MLP) with Backpropagation learning algorithm, Self Organizing Map (SOM) and Recurrent Neural Network (RNN). Support Vector Machines (SVM) have gained great interest in the last few years in pattern recognition. Thus, this research compares the recognition performance of text and non-text images (text, table, figure and graph) from technical document images based on the pixel intensity of various zones between BPNN, SOM, RNN and SVM. Symmetrical and non-symmetrical zoning algorithms were compared as input. 400 different datasets have been tested and the experiments indicate that SVM classification is superior to the other three classifiers. The experiments also indicate that the combination of symmetrical and non-symmetrical zoning design is better than non-symmetrical or symmetrical zoning only.