|吴少泓; 王云宽; 孙涛
|Other Abstract||Due to the mutability of unstrained or handwritten digits, most algorithms in previous works either forfeited easy implementation for high accuracy, or vice versa. In this paper, we proposed a novel feature descriptor，distance distribution histogram(DDH) and another adapted shape accumulate histogram(SAH) feature descriptor based on shape context which was not only easy to implement, but also was robust to noise and distortion. To make hybrid features more comprehensive, we also combined other adapted topological features. The new congregated features were complementary as they were formed from different original feature sets extracted by different means. What’s more, they were not complicate. We then employed three support vector machines (SVMs) with different feature vector doing recognition and integrated their results to get the final classification. Best result among several experiments based on different data bases was 99.213%, which demonstrated that our proposed algorithm was very robust and effective.|
吴少泓,王云宽,孙涛. 基于距离分布直方图的数字识别算法[J]. 计算机应用,2012,8(32):2299－2304.
吴少泓,et al."基于距离分布直方图的数字识别算法".计算机应用 8.32(2012):2299－2304.
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