Many researches have been conducted on the recognition of Thai characters. Different approaches, such as neural network, syntactic, and structural methods, have been proposed. However, the success in recognizing Thai characters is still limited, compared to English characters. This paper proposes an approach to recognize the printed Thai characters using the hybrid of global feature, local features, fuzzy membership function and the neural network. The global feature classifies all characters into seven main groups. Then the local features and the neural network are applied to identify the characters.

This work provides a generalization of structural logic optimization methods to general boolean networks. This generalization is based on a functional description of the nodes in the network. Therefore, this approach is no longer restricted to networks that consist of simple gates. Within this framework, we present necessary and sufficient conditions to identify all the possible functional expansions of a node that allow to eliminate a wire elsewhere in the network. These conditions are also given for the case of multiple variable expansion, providing an incremental mechanism to perform functional transformations involving any number of variables that can be applied in a very efficient manner. On the other hand, we will show in this paper that relevant simplifications can be obtained when this framework is applied to the particular case of AND-OR-NOT networks, resulting in important savings in the computational effort. When compared to previous approaches, the experimental results show an important reduction in the number of computations required.