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.

Persian Optical Character Recognition Using Deep Bidirectional Long Short-Term Memory

“…Authors have found that DenseNet is more suitable for a Persian OCR system because it has less learnable parameters which makes it less prone to overfitting. Another usage of a well known CNN-based network in Persian OCR system can be found in [38] where authors have used a LeNet [39] optimized by meta heuristic training in their system. Authors have utilized four algorithms of firefly algorithm, ant colony optimization, chimp optimization algorithm (ChOA), and particle swarm optimization to optimize LeNet's weights and concluded that ChOA serves this purpose better than the other algorithms.…”

Offline Persian Handwriting Recognition with CNN and RNN-CTC

“…Deep Learning has had a huge impact on handwriting recognition applications in different languages as well as on Persian handwritten recognition [23,24,25,26,27,28]. Here deep models are trained using the words, letters, and digits in the Khayyam dataset.…”