Particle Swarm Optimization with Random Forests for Handwritten Arabic Recognition System

Abstract

There are many problems facing the processing of a handwritten Arabic recognition system such as unlimited variation in human handwriting, similarities of character shapes and their position in the word. This paper presents a handwritten Arabic character recognition system based on Particle Swarm Optimization with random Forests. The main objective of the proposed system is to improve the recognition rate and reduce the feature set size. The proposed system is trained and tested by a well-known classifier; Random forests (RF) on CENPRMI dataset. The proposed optimization algorithm obtained promising results in terms of classification accuracy as the proposed system is able to recognize 91.66 % of our test set correctly, as well as, it reduced the computational time. When comparing our results with other related works we find that our results is the highest among other published results.