This paper proposes a blended model that is suitable for recognizing off-line handwritten accented characters in general and Vietnamese characters in particular. The recognition model linearly combines four extracting methods in the feature extraction period, including Zones density, Projection histogram, Contour profiles, and Haar wavelets. The set of features obtained will be applied with Principal Component Analysis (PCA) to retain useful features, reducing the recognition time. Additionally, a Support Vector Machine (SVM) is also utilised for training and recognition. The proposed model is tested on the dataset of 21174 samples with 99 Vietnamese off-line handwritten accented characters.