关键词: 手写签名, 局部中心点特征, 绝对距离, cosine距离

Abstract:  Being an identity authentication method, signature recognition has widely used and plays an important role in the all areas and sectors of modern society. Regarding to the shortcoming of single low-dimensional features which have caused the low signature recognition rate, this paper proposes a high-dimensional statistical features based Uyghur handwritten signature recognition method. According to the demand of feature extraction, preprocessing steps such as smoothing, binarization, normalization and thinning are conducted to each signature image firstly. Then 128 dimensional local center point features and 112 dimensional ETDT features are extracted separately and they are combined to form a new high-dimensional feature. Finally, distance measurement and similarity measurement methods are used for training and recognition process. The experimental results show that the proposed method obtains much better recognition rate than earlier methods, and improves the accuracy of Uyghur handwritten signature recognition effectively.

Key words: handwritten signature, local central point feature, absolute distance, cosine distance