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On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net
| Citations: | 1 - 0 self |
BibTeX
@MISC{Zafar_on-linehandwritten,
author = {Muhammad Faisal Zafar and Dzulkifli Mohamad and Razib M. Othman},
title = {On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net},
year = {}
}
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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.
Keyphrases
on-line handwritten character recognition counterpropagation neural net recognition mechanism multiple threshold counter-propagation neural network pen tip trace obtained recognition rate keywords on-line character recognition significant effect performance study abstract on-line handwritten script upper-case english alphabet pen coordinate useful character information counter propagation neural network recognition process different people size normalization lengthy preprocessing trajectory information different set extreme coordinate pen-up position feature extraction mechanism different style off-line character recognition full detail counter-propagation architecture time evaluation character sample slant removing character digitization simple approach
