Recognition of handwritten Persian/Arabic numerals by shadow coding and an edited probabilistic neural network

A system for recognition of segmented handwritten Persian/Arabic numerals irrespective of size and translation is developed. The image is represented by invariant features obtained from a new shadow coding scheme designed for the considered shapes. Classification is performed by a modified version of a four-layer probabilistic neural network (PNN) called the edited PNN (EPNN). Due to an editing and condensation procedure on the training samples, the EPNN has better performance and the network size is smaller. The performance of the system is evaluated on a database consisting of 2600 digits written by 10 different people. The obtained recognition accuracy is 97.8 percent. The developed system can process approximately two digits per second on a Intel 486 based PC with a 66 MHz clock.