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On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net

By Muhammad Faisal Zafar, Dzulkifli Mohamad and Razib M. Othman

Abstract

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. Keywords—On-line character recognition, character digitization, counter-propagation neural networks, extreme coordinates I

Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.135.9720
Provided by: CiteSeerX
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