Zafar, Muhammad Faisal and Mohamad, Dzulkifli and Othman, Muhamad Razib (2005) On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net. On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net, V10 . pp. 232-237. ISSN 1305-5313
Official URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=1...
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 hresholds 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.
|Uncontrolled Keywords:||On-line character recognition, character digitization, counter-propagation neural networks, extreme coordinates|
|Divisions:||Computer Science and Information System (Formerly known)|
|Deposited By:||Dr Muhamad Razib Othman|
|Deposited On:||12 Apr 2017 01:31|
|Last Modified:||12 Apr 2017 01:31|
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