基于结构特征的多字体多字号卡纳达数字识别
Multi-font Multi-size Kannada Numeral Recognition Based on Structural Features
摘要：
本文提出了一种快速、新颖的多字体、多字号卡纳达数字识别方法，该方法无需细化和尺寸归一化处理。数字识别采用不同的结构特征，即四个方向上的像素方向密度、水库、最大剖面距离和填充孔密度。利用NUDI和BARAHA流行的字处理卡纳达语软件中的20种不同字体的16 ~ 50种不同字体大小的数字图像，采用欧氏最小距离准则寻找最小距离，采用K-近邻分类器对卡纳达语数字进行分类。对总共1150幅数字图像进行了测试，分类的总体准确率为100%。该方法所用的平均时间为0.1476秒。
英文摘要：
In this paper a fast and novel method is proposed for multi-font multi-size Kannada numeral recognition which is thinning free and without size normalization approach. The different structural feature are used for numeral recognition namely, directional density of pixels in four directions, water reservoirs, maximum profile distances, and fill hole density are used for the recognition of Kannada numerals. A Euclidian minimum distance criterion is used to find minimum distances and K-nearest neighbor classifier is used to classify the Kannada numerals by varying the size of numeral image from 16 to 50 font sizes for the 20 different font styles from NUDI and BARAHA popular word processing Kannada software. The total 1150 numeral images are tested and the overall accuracy of classification is found to be 100%. The average time taken by this method is 0.1476 seconds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
