Increasing the feature size in handwritten numeral recognition to improve accuracy

Abstract

Abstract The relationship between the recognition rate of handwritten numerals and the normality of the distribution of their features has been investigated experimentally with a large amount of data in various combinations of quantized orientations and regions. The recognition method is based on the histogram of local orientation of contours of each numeral. To obtain a more accurate orientation quantization, the effectiveness of the orientation quantization using the gray‐scale gradient has also been investigated. The results show that: (1) to increase the dimensionality of features, it is better to increase the number of quantized orientations, keeping the number of regions small (e.g., 4 × 4 or 5 × 5); (2) in the same dimensionality, the better the normality of a feature distribution, the higher the recognition rate; (3) a quantization of orientations using gray scales is effective for normalizing a feature distribution; and (4) the filter processing in reduction of the number of quantization scales improves the normality and recognition rate. The recognition of handwritten numerals collected from actual posts were carried out by using the gray‐scale local‐orientation histogram (400 dimensions). A correct recognition rate of 99.18 percent (mean value) has been obtained.