A Learning OCR System Using Short/Long-term Memory Approach and Hardware Implementation in FPGA

In this paper we propose a learning OCR system which is based on taking a short and long-term memory and a ranking mechanism which manages the transition of reference patterns between two memories. Also, an optimization algorithm is used to optimize the reference vectors magnitude as well as their distribution, continuously. The system was implemented in the FPGA platform and was tested with some real test data of Roman fonts and the classification results found acceptable. LSI architecture Comparing to other learning models like neural networks or A-means, the main advantage of the proposed algorithm is its simple design which makes it implementable in the hardware especially LSI architecture with no need to a large amount of resources.