关键词: 特征提取, 笔迹鉴别, Gabor滤波器, 高斯马尔科夫随机场（GMRF）

Abstract: Extracting effective features to describe handwriting is always a key problem in writer identification. In order to overcome the shortcomings of the traditional Gabor filter method, as well as to fully exploit correlation between Gabor filtering coefficient, this paper proposes a novel method for handwriting feature extraction, which merges the global and local features together. Histogram Of Gradient （HOG） of the character strokes is firstly used to optimize the orientations of Gabor filter, then Gauss Markov Random Field （GMRF） models are developed for every filtered image to describe the different local spatial structures, and finally it obtains the overall style characteristics of the handwriting images. With the four most famous regular script writers’ original samples and the collected English scripts as the experimental data, the minimum weighted Euclidean distance classifier is applied to classify handwriting samples, respectively achieving correct classification rates of 97.6% and 88.3% with five-fold cross validation method, which shows that the extracted features have strong ability to characterize the handwriting, and the proposed method is effective in handwriting feature extraction.

Key words: feature extraction, writer identification, Gabor filter, Gauss Markov Random Field（GMRF）