TY - JFULL AU - Velappa Ganapathy and Kok Leong Liew PY - 2008/4/ TI - Handwritten Character Recognition Using Multiscale Neural Network Training Technique T2 - International Journal of Computer and Information Engineering SP - 637 EP - 643 VL - 2 SN - 1307-6892 UR - https://publications.waset.org/pdf/2037 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 15, 2008 N2 - Advancement in Artificial Intelligence has lead to the developments of various “smart" devices. Character recognition device is one of such smart devices that acquire partial human intelligence with the ability to capture and recognize various characters in different languages. Firstly multiscale neural training with modifications in the input training vectors is adopted in this paper to acquire its advantage in training higher resolution character images. Secondly selective thresholding using minimum distance technique is proposed to be used to increase the level of accuracy of character recognition. A simulator program (a GUI) is designed in such a way that the characters can be located on any spot on the blank paper in which the characters are written. The results show that such methods with moderate level of training epochs can produce accuracies of at least 85% and more for handwritten upper case English characters and numerals. ER -