@article{(Open Science Index):https://publications.waset.org/pdf/1311, title = {On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net}, author = {Muhammad Faisal Zafar and Dzulkifli Mohamad and Razib M. Othman}, country = {}, institution = {}, abstract = {On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple thresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples. }, journal = {International Journal of Computer and Information Engineering}, volume = {1}, number = {10}, year = {2007}, pages = {3291 - 3296}, ee = {https://publications.waset.org/pdf/1311}, url = {https://publications.waset.org/vol/10}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 10, 2007}, }