other hand, new understanding of the neural network emerges and may extend the ability of DNN. For example, in Ian Goodfellow’s recent work [@Goodfellow] for digit string recognition, the output layer is trained to show both the digit number and the recognition result of each digit. By doing this, their DNN model is able to recognize the digit string directly, without any segmentation process. This work extended the DNN from single character recognition to character string recognition. Inspired by this work, we may change the basic framework of DNN to find more possibilities.

In this paper, we focus on the basic function of ANN and try to make it more suitable for computers. Usually, the ANN is seen as simulation of natural neural process, thus its neurons and weights are all real number. Such values can represent the electric signal generated by neural cells. However, the ANN models are often realized by computers. As we all know, the computer process data based on binary value, like “0” and “1”. In other words, we can say that the basic “neural cell” of computer generates binary signals. Inspired by this observation, we proposed a new type of neural network — the binarized 