A computer-implemented method of extending an artificial neural network to improve analysis accuracy of the artificial neural network utilized in training based recognition, the method comprising: storing the artificial neural network in a memory (1920, 2020); selecting, with a processor, a node in a hidden layer of the artificial neural network based on activation information; adding a new node in the hidden layer that includes the selected node; connecting the new node to nodes (701 - 703) included in a layer preceding the hidden layer; connecting the new node to nodes included in a layer subsequent to the hidden layer; setting connection weights of the new node; and adjusting the connection weights of the new node by training the artificial neural network extended to include the new node with training data comprising digital images of handwritings and corresponding text characters recognized from the images; wherein the setting comprises: selecting a portion of connection weights (622, 623) of the selected node; setting connection weights (722, 723) of the new node that correspond to the selected portion of connection weights (622, 623) of the selected node to be equal to the selected portion of connection weights (622, 623) of the selected node; setting remaining connection weights (721, 711 - 713) of the new node to a predetermined initial value; and setting the selected portion of connection weights (622, 623) of the selected node to the predetermined initial value. storing the artificial neural network in a memory (1920, 2020); selecting, with a processor, a node in a hidden layer of the artificial neural network based on activation information; adding a new node in the hidden layer that includes the selected node; connecting the new node to nodes (701 - 703) included in a layer preceding the hidden layer; connecting the new node to nodes included in a layer subsequent to the hidden layer; setting connection weights of the new node; and adjusting the connection weights of the new node by training the artificial neural network extended to include the new node with training data comprising digital images of handwritings and corresponding text characters recognized from the images; wherein the setting comprises: selecting a portion of connection weights (622, 623) of the selected node; setting connection weights (722, 723) of the new node that correspond to the selected portion of connection weights (622, 623) of the selected node to be equal to the selected portion of connection weights (622, 623) of the selected node; setting remaining connection weights (721, 711 - 713) of the new node to a predetermined initial value; and setting the selected portion of connection weights (622, 623) of the selected node to the predetermined initial value. selecting a portion of connection weights (622, 623) of the selected node; setting connection weights (722, 723) of the new node that correspond to the selected portion of connection weights (622, 623) of the selected node to be equal to the selected portion of connection weights (622, 623) of the selected node; setting remaining connection weights (721, 711 - 713) of the new node to a predetermined initial value; and setting the selected portion of connection weights (622, 623) of the selected node to the predetermined initial value.