Abstract
Feature extraction is one of the most important factors for recognition system based on hidden Markov models (HMMs). This paper presents a new approach by using background feature and HMMs for off-line handwritten character recognition. The background feature for white pixels (also called background pixels) is based on the Freeman code with eight directions, which is an improvement of Alceu's method with four directions. Experimental results for off-line handwritten Chinese legal amount show the validity of the new approach. The recognition rate is about 1.8~4.9% higher than that of Alceu's method with the same HMMs topology. For the new approach within the tested topologies, the highest recognition rate can be 96.39%.
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