Chimp Optimization Algorithm to Optimize a Convolutional Neural Network for Recognizing Persian/Arabic Handwritten Words

Abstract

Handwritten character recognition is an attractive subject in computer vision. In recent years, numerous researchers have implemented techniques to recognize handwritten characters using optical character recognition (OCR) approaches for many languages. One the most common methods to improve the OCR accuracy is based on convolutional neural networks (CNNs). A CNN model contains several kernels accompanying with pooling layers and nonlinear functions. This model overcomes the problem of adjusting the value of weights and interconnections of the neural network (NN) for creating an appropriate pipeline to process the spatial and temporal information. However, the training process of a CNN is a challenging issue. Various optimization strategies have been recently utilized for optimizing CNN’s biases and weights such as firefly algorithm (FA) and ant colony optimization (ACO) algorithms. In this study, we apply a well-known nature-inspired technique called chimp optimization algorithm (ChOA) to train a classical CNN structure LeNet-5 for Persian/Arabic handwritten recognition. The proposed method is tested on two known and publicly available handwritten word datasets. To deeply investigate and evaluate the approach, the results are compared with three optimization methods including ACO, FA, and particle swarm optimization (PSO). Outcomes indicated that the proposed ChOA technique considerably improves the performance of the original LeNet model and also shows a better performance than the others.