HAND-WRITING RECOGNITION USING NEURAL MICRO-CLASSIFIERS NETWORK | ||||
| Journal of the ACS Advances in Computer Science | ||||
| Article 6, Volume 9, Issue 1, 2018, Page 91-107 PDF (372.21 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/asc.2018.158384 | ||||
 View on SCiNiTO | ||||
| Abstract | ||||
| In this study, a hand writing recognition methodology based on the neural binary micro-classifier network. The proposed methodology uses simple well known feature extraction methodology. The feature extraction used is the discrete cosine transformation low frequencies coefficients. The micro-classifier network is a deterministic four layers neural network, the four layers are: input, micro-classifier, counter, and output. The network provide confidence factor, and proper generalization is guaranteed. Also, the network allows incremental learning, and more natural than others. The recognition methodology was tested using the standard MNIST dataset. The experimental results of the methodology showed comparative performance taking in consideration the design advantages. | ||||
| Keywords | ||||
| Neural networks; Feature extraction; Image processing; DCT; MNIST; HAND WRITING | ||||
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