Classification Of Gradient Change Features Using MLP For Handwritten Character Recognition

Details Classification Of Gradient Change Features Using MLP For Handwritten Character Recognition Classification Of Gradient Change Features Using MLP For Handwritten Character Recognition

2010-06-30

arxiv.org/abs/1006.5927v1

EAIT 2006

Computer Science

Computer Vision and Pattern Recognition

Scientific paper

A novel, generic scheme for off-line handwritten English alphabets character images is proposed. The advantage of the technique is that it can be applied in a generic manner to different applications and is expected to perform better in uncertain and noisy environments. The recognition scheme is using a multilayer perceptron(MLP) neural networks. The system was trained and tested on a database of 300 samples of handwritten characters. For improved generalization and to avoid overtraining, the whole available dataset has been divided into two subsets: training set and test set. We achieved 99.10% and 94.15% correct recognition rates on training and test sets respectively. The purposed scheme is robust with respect to various writing styles and size as well as presence of considerable noise.

Affiliated with

Also associated with

No associations

Rating

Classification Of Gradient Change Features Using MLP For Handwritten Character Recognition does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Classification Of Gradient Change Features Using MLP For Handwritten Character Recognition, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Classification Of Gradient Change Features Using MLP For Handwritten Character Recognition will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-154482