基于LTP和HOG纹理特征融合的中亚文档图像文种识别

Script identification of Central Asian document images based on LTP and HOG texture feature fusion

针对中亚地区存在一些相似度较高的文种,提出一种基于具有旋转不变性的统一局部三值模式(rotation invariant uniform local ternary pattern, riu2-LTP)和方向梯度直方图(histogram of oriented gradients, HOG)特征交叉融合的文档图像文种方法。使用SVM分类器对包含10个文种共10 000张图片的数据库进行试验;为了提高多文种识别效果,采用贝叶斯优化SVM的超参数。对文档图像提取了半径为1,采样点为8的riu2-LTP;重新对数据库提取HOG;采用交叉融合方法将20维riu2-LTP特征与36维HOG特征分别依次融入到新的特征集。试验表明,本研究方法平均查准率达到99%,相较于单一LTP、riu2-LTP和HOG方法有更好性能。

Due to the existence of a number of scripts with high similarity in Central Asia, a document image script identification method based on the cross-fusion of a unified local ternary pattern(riu2-LTP) with rotational invariance and histogram of oriented gradients(HOG) features was proposed. An SVM classifier was used to perform experiments on a database containing a total of 10 000 images of 10 scripts. In order to improve multi-script identification, Bayesian optimized SVM hyperparameters were used. The method first extracted riu2-LTP with a radius of and a sampling 8 points for the document images;HOG was extracted from the database again;the cross-fusion method was to incorporate the 20-dimensional riu2-LTP features and 36-dimensional HOG features sequentially into the new feature set, respectively. The experiments showed that the average recognition rate of this method reached 99%, which was better than the single LTP, riu2-LTP, and HOG methods.