Off-Line Handwritten Signature Recognition Based on Discrete Curvelet Transform

Abstract

In order to improve the offline handwritten signature recognition effect, an offline handwritten signature recognition method based on discrete curvelet transform is proposed. First, the necessary pre-processing of offline handwritten signatures is carried out, including grayscale, binarization, smooth denoising, etc. The pre-processed signature image is subjected to curvelet transform to obtain real-numbered curve coefficients in the cell matrix, and a total of 82-dimensional energy features are extracted, and multi-scale block local binary mode (MBLBP) is combined on the cell matrix of discrete curvelet transform to form a new signature feature, use the SVM classifier for training and classification. Experiments on two databases, Uyghur and Kirgiz, the highest accuracy was 97.95% and 97.42% respectively. The experimental results show that the proposed method has better accuracy in offline handwritten signature recognition.