COMPARATIVE STUDY OF FONT RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS AND TWO FEATURE EXTRACTION METHODS WITH SUPPORT VECTOR MACHINE

Font recognition is one of the essential issues in document recognition and analysis, and is frequently a complex and time-consuming process. Many techniques of optical character recognition (OCR) have been suggested and some of them have been marketed, however, a few of these techniques considered font recognition. The issue of OCR is that it saves copies of documents to make them searchable, but the documents stop having the original appearance. To solve this problem, this paper presents a system for recognizing three and six English fonts from character images using Convolution Neural Network (CNN), and then compare the results of proposed system with the two studies. The first study used NCM features and SVM as a classification method, and the second study used DP features and SVM as classification method. The data of this study were taken from Al-Khaffaf dataset [21]. The two types of datasets have been used: the first type is about 27,620 sample for the three fonts classification and the second type is about 72,983 sample for the six fonts classification and both datasets are English character images in gray scale format with 8 bits. The results showed that CNN achieved the highest recognition rate in the proposed system compared with the two studies reached 99.75% and 98.329 % for the three and six fonts recognition, respectively. In addition, CNN got the least time required for creating model about 6 minutes and 23- 24 minutes for three and six fonts recognition, respectively. Based on the results, we can conclude that CNN technique is the best and most accurate model for recognizing fonts

Numerical Solution of Kawahara Equation Using Neural Network

An artificial neural network technique is proposed in this research to solve the well-known partial differential equations of the types: Kawahara and modified Kawahara equations. The mathematical model of the equation was developed with the help of artificial neural networks. The construction requires imposing certain constrains on the values of the input, bias and output weights, and on the attribution of certain roles of each aforementioned parameters. The results obtained from the proposed technique were very accurate, simple and convenient. Moreover, the comparison between the approximated solutions and the exact one has done. This comparison found them in a good agreement with each other due to of superior properties of the Neural Network

Natural Resources, Volatility, and Inclusive Growth: Perspectives From the Middle East and North Africa

This paper takes stock of the economic performance of resource rich countries in the Middle East and North Africa (MENA) over the past forty years. While those countries have maintained high levels of income per capita, they have performed poorly when going beyond the assessment based on standard income level measures. Resource rich countries in MENA have experienced relatively low and non inclusive economic growth as well as high levels of macroeconomic volatility. Important improvements in health and education have taken place but the quality of the provision of public goods and services remains an important source of concerns. Looking forward we argue that the success of economic reforms in MENA rests on the ability of those countries to invest boldly in building inclusive institutions as well as high levels of human capacity in public administrations