Personal Dictionaries for Handwritten Character Recognition Using Characters Written by a Similar Writer

We propose two new generation methods of personal dictionary for handwritten character recognition using the set of characters written by a similar writer. The methods employ only one character written by one specific writer, and its character selects the set of characters written by the similar writer to generate the personal dictionary for the specific writer. The first type (similar mean) dictionary uses the mean feature vector of a similar writer which is selected by only one specific writer´s character. The second type (similar feature space) dictionary uses the mean feature vector and the covariance matrix of the selected similar writer. We compared the effect for handwritten Japanese “HIRAGANA” characters. The similar feature space dictionary obtained the recognition rate 91% relatively to the rate of a general dictionary 82 %. It is confirmed that only one character by a specific writer is very effective on personal character recognition.