An Artificial Neural Networks Based OCR System And Its Hardware Implementation

After many years research, the artificial neural networks already widely used for to solve many complex problems in pattern recognition and artificial intelligence domain, and has got the success which the traditional algorithm was hard to obtain. The research of OCR (Optical character recognition) is an important branch on pattern recognition domain. Very natural, we introduce the artificial neural networks into OCR algorithm design. Since last century 70's, the OCR technology has gradually moved to maturely. For now, the performance of the printed character recognition is high, and the small-scale handwritten character recognition works very well in practice.Compared with the large-scale character recognition system (for example, the Chinese character recognition), the small-scale character recognition system is more easily to be realized, and it is widely used in daily life. For example, the Arabic numeral recognition system, only carries on the recognition to Arabic numeral, is applied in so many domain, such as zipcode recognition, the license plate recognition, product serial number recognition on assembly line, etc. The topic of our study is using artificial neural networks to realize the small-scale character recognition system.For present, as a result of the progress of semiconductor technology, the MCU become cheaper and quicker. Using these new MCU, we can easily design the high performance embedded system in industrial domain or household appliances. From the cost and the size consideration, we use ARM based embedded system to implement our OCR system.Our work may divide into two topics: hardware and the software. Our study and innovation in this paper as follow:1. We studied the architecture of the ARM based embedded system.a) Using the Atmel's AT91RM9200, to design our embedded system.b) Studying the craft and design of the PCB in high frequence signal system.c) Studying the Linux operation system, transplanting the Linux system into our system.2. We realized the artificial neural networks based optical character recognition algorithm.a) Studying the image extraction, image-preprocessing algorithm.b) Studying the K-L transformation applied in character features extraction.c) Using the US postal service database, training our BP network based classifier. (After training, we obtain a Arabic numeral classifier with 92% correct rate)d) Studying other some classifiers compared the performance of those classifiers with our BP network classifiers.