TAF neural network for handwritten digits recognition

This paper investigates the application of TAF (trainable activation function) neural network to handwritten digit recognition. A three-layer feedforward TAF neural network is used as digits recognizer. Each TAF neuron in the hidden layer acts as a two-class classifier which distinguishes the two pattern classes from each other. The output layer gives output for decision making. While many approaches have bean suggested for this application the proposed method is new and has the advantage of small network size and good recognition performance. Experiments performed on selected digits from NIST database demonstrated that about 99% correct recognition rate has been achieved.