A NEW HIERARCHICAL APPROACH FOR RECOGNITION OF UNCONSTRAINED HANDWRITTEN NUMERALS

In this paper, a new hierarchical approach for recognition of the unconstrained handwritten numerals is proposed. In order to obtain a reliable skeleton of the observed character, some preprocessing operations including smoothing, noise removing, normalization, and thinning process are firstly applied to each character. Then, some interesting feature points are extracted from this reliable skeleton of the character. In the first stage of preclassification, a set of structural features named four-zone codes is adopted to preclassify the numerals. Due to the large degree of data and distortion of characters, a three layer of fuzzy neural network is used for fine classification. Experimental results show that a high recognition rate over 99.5% is obtained in this work.