计算机科学
模式识别(心理学)
人工智能
手写体识别
特征(语言学)
二叉树
算法
新认知
人工神经网络
数字识别
特征提取
数字系统
二进制数
时滞神经网络
数学
算术
哲学
语言学
作者
Kai-lin He,Jia Luo,Xiaoqing Ding
标识
DOI:10.1109/icmra53481.2021.9675509
摘要
A handwritten numeric recognition algorithm based on holistic feature (concave and convex) is proposed in document [6], Although the effect is good, this algorithm is only suitable for the sample of the writing specification, in real life, the numbers have a variety of deformations by the randomness of people’s writing, it affects the recognition rate of this algorithm. This paper is proposed a novel off-line handwritten numeric recognition algorithm based on binary classification tree. It is improvement through background correction improvement, concave and convex features improvement and classification tree improvement. The experimental results show that the algorithm in this paper has been improved in the original recognition rate and is more adaptive to the deformation of handwritten numerals. The recognition of handwritten numbers is more extensive. At the same time, compared with other popular contour based features and neural network based algorithms, there is an obvious advantage in speed.
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