A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: study case on Malay word recognition

Abstract

An off-line handwriting recognition (OFHR) system is a computerized system that is capable of intelligently converting human handwritten data extracted from scanned paper documents into an equivalent text format. This paper studies a proposed OFHR for Malaysian bank cheques written in the Malay language. The proposed system comprised of three components, namely a character recognition system (CRS), a hybrid decision system and lexical word classification system. Two types of feature extraction techniques have been used in the system, namely statistical and geometrical. Experiments show that the statistical feature is reliable, accessible and offers results that are more accurate. The CRS in this system was implemented using two individual classifiers, namely an adaptive multilayer feed-forward back-propagation neural network and support vector machine. The results of this study are very promising and could generalize to the entire Malay lexical dictionary in future work toward scaled-up applications.

A Machine Learning Model for Determination of Gender Utilizing Hybrid Classifiers

“…The Hybrid ANN SVM model proved to be faster in the testing phase and was able to improve classification performance by reducing the error rate. The combined approach of ANN and SVM classifiers integrated in one hybrid system has been successfully applied to classify the location of damaged parts in aircraft gas turbine engines, perform soil moisture content prediction, and handle visual and recognition tasks in the context of robotic swarm use (Al-Boeridi et al, 2015).…”