Isolated Persian/Arabic handwriting characters: Derivative projection profile features, implemented on GPUs

For many years, researchers have studied high accuracy methods for recognizing the handwriting and achieved many significant improvements. However, an issue that has rarely been studied is the speed of these methods. Considering the computer hardware limitations, it is necessary for these methods to run in high speed. One of the methods to increase the processing speed is to use the computer parallel processing power. This paper introduces one of the best feature extraction methods for the handwritten recognition, called DPP (Derivative Projection Profile), which is employed for isolated Persian handwritten recognition. In addition to achieving good results, this (computationally) light feature can easily be processed. Moreover, Hamming Neural Network is used to classify this system. To increase the speed, some part of the recognition method is executed on GPU (graphic processing unit) cores implemented by CUDA platform. HADAF database (Biggest isolated Persian character database) is utilized to evaluate the system. The results show 94.5% accuracy. We also achieved about 5.5 times speed-up using GPU.