Journal of Computer Science 5 (6): 427-434, 2009 ISSN 1549-3636 © 2009 Science Publications Cxorresponding Author: Saleh Ali K. Al-Omari, School of Computer Sciences, University Sains Malaysia, 11800 Penang, Malaysia 427 Digital Recognition using Neural Network Saleh Ali K. Al-Omari, Putra Sumari, Sadik A. Al-Taweel and Anas J.A. Husain School of Computer Sciences, University Sains Malaysia, 11800 Penang, Malaysia Abstract: Problem statement: Handwriting number recognition is a challenging problem researchers had been research into this area for so long especially in the recent years. In our study there are many fields concern with numbers, for example, checks in banks or recognizing numbers in car plates, the subject of digit recognition appears. A system for recognizing isolated digits may be as an approach for dealing with such application. In other words, to let the computer understand the Arabic numbers that is written manually by users and views them according to the computer process. Scientists and engineers with interests in image processing and pattern recognition have developed various approaches to deal with handwriting number recognition problems such as, minimum distance, decision tree and statistics. Approach: The main objective for our system was to recognize isolated Arabic digits exist in different applications. For example, different users had their own handwriting styles where here the main challenge falls to let computer system understand these different handwriting styles and recognize them as standard writing. Result: We presented a system for dealing with such problem. The system started by acquiring an image containing digits, this image was digitized using some optical devices and after applying some enhancements and modifications to the digits within the image it can be recognized using feed forward back propagation algorithm. The studies were conducted on the Arabic handwriting digits of 10 independent writers who contributed a total of 1300 isolated Arabic digits these digits divided into two data sets: Training 1000 digits, testing 300 digits. An overall accuracy meet using this system was 95% on the test data set used. Conclusion: We developed a system for Arabic handwritten recognition. And we efficiently choose a segmentation method to fit our demands. Our system successfully designs and implement a neural network which efficiently go without demands, after that the system are able to understand the Arabic numbers that was written manually by users. Key words: Neural network, ANN, segmentation, digital recognition, feed forward back propagation algorithm INTRODUCTION Recently, a lot of works was done by depending on the computer; In order to let the processing time to be reduced and to provide more results that are accurate, for example, depending on different types of data, such as characters and digits and the numbers are used frequently in normal life operation. In order to automate systems that deal with numbers such as postal code, banking account numbers and numbers on car plates. And an automatic recognition number system is proposed in this study. Digit recognition has been extremely found and studied. Various approaches in image processing and pattern recognition have been developed by scientists and engineers to solve this problem [1,16] . That is because it has an importance in several fields and it may probably be used in checks in banks or for recognizing numbers in cars plates, or many other application. In this study, system for recognized of digits is built, which may benefit various fields, the system concerning on isolated digits, the input is considered to be an image of specific size and format, the image is processed and then recognized to result of an edited digits. The proposed system recognizes isolated Arabic digits as the system acquire an image consisting digits, then, the image will be processed into several phases such as image enhancement, thinning, skeletonaization and segmentation before recognizing the digit. A multilayer neural network will be used for the recognition phase; a feed forward back propagation