Recognizing Handwritten Digits Using Hierarchical Products of Experts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
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This paper studies the recognition of Persian handwritten characters using templates and back propagation networks. The last one is learned by gradient decent learning law which was promoted by adaptive learning rate and momentum. Different technical methods which are often based on artificial neural network or neuro-fuzzy ones are used in recognition characters. Often the whole data is squeezed in the aforementioned networks which are to classify the data to each of existed classes. However, in this paper the templates are used for the primary classification of data. So, using templates leads to the recognition of some squeezed data and classification of remaining one into smaller common classes before feeding them into neural network; then the neural network is used for final classification. The results show that there are some significant improvements on recognition performance. This happens because decreasing input and output space causes to have a simpler mapping between input and output which in turn lead to increasing learning rate and decreasing classification error. Recognition rate on our dataset is almost 100%.