Improved the Automatic Number Plate Recognition System (ANPR)

The Automatic recognition System to vehicles through its number is an important topic, because of its important uses, such as security applications by monitoring the entrances of a important institutions, monitor the vehicles on the road, detection of stolen cars, and even that could be useful in statistical studies, where we can study the traffic congestion in an area. In this work we offer an overview of the Automatic Number Plate Recognition System (ANPR) through to identify the license plate number, and also recognize the color of car. The focus of this research on the stage of converting the numbers into a picture of a car plate to actual figures, to improve the performance of all system, where many of errors that occur at this stage. In this search was used the algorithm of Principle component analysis (PCA) to identify the numbers plate inside the picture. and its integration with optical character Recognition algorithm(OCR) which usually used for recognition , to minimize errors in recognition numbers and thus improve the performance of the automatic number plate system.and also we add color car recognize(which another important parameter of car) , this helps after return to data base detect stolen vehicles and improve the reliability of system

Recognition of Hand Written Arabic Names Using Deep Learning

Designing Computerized Systems which posses reading and hearing faculties is an active research area for more than four decades. Many methods and algorithms have been suggested by researches for this purpose as part of pattern recognition research. Recently, more research work has been devoted to the holist approach the recognition system recognizes a complete word as one object without going through the long and erroneous character segmentation process. In this paper, a convolutional neural network has been designed to recognize the popular Arabic names holistically. SUSt ARG names data set has been used to test the network performance (collected and compiled by pattern recognition research in Sudan University of Science and Technology-SUSt). Selecting an appropriate deep learning toolbox, after five stages of training, the network was able to recognize all the names and 100%