Off-line Uyghur signature recognition based on modified grid information features

Many techniques have been published on handwriting signature recognition, but none of these techniques presented are about Uyghur handwritten signature due to its complex nature. In this paper, we propose methods for off-line signature recognition for Uyghur handwriting first time. The signature images were pre-processed based on the nature of Uyghur signature. The preprocessing included noise reduction, binarization and normalization. Then multi-dimensional modified grid information features were extracted according to the character of Uyghur signature and its writing style. Finally, three kinds of classification techniques were used: Euclidean distance (ED) classifier, K nearest neighbor (K-NN) classifier and Bayes classifier. Experiments were performed using Uyghur signature samples from 50 different people with 1000 signatures. A promising result of 93.53% average correct recognition rate was achieved.