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One of the essential distinctions between different fonts is their stroke shape. A method is presented to automatically extract representative stroke templates from a text image, which contains characters of the same typeface. The collected stroke templates are classified and saved to a font database. To recognize an unknown font for an input text image, a Bayes decision rule is used to determine which font entrant in the database provides the best matching to the unknown font. The experiment demonstrates that this approach can distinguish between Chinese and English fonts without the prior information of their script. Another advantage is that it can learn a new font very quickly. Forty fonts (twenty English and twenty Chinese) are used in our experiment. An average recognition accuracy of 97 percent can be achieved in the present system.

The recognition accuracy of adult image groups depends on the performance of the adult image recognizer and the final decision rule. Earlier methods of recognizing adult image groups do not take into account the performance tuning of the adult image recognizer but only focus on the decision rule. The proposed method considers the two factors together and resolves optimal parameter settings to achieve the best recognition accuracy for image groups. Experimental results show that the proposed method can attain higher recognition accuracy than the earlier methods.