计算机工程与科学2018，Vol.40Issue(1)：79-85,7.DOI:10.3969/j.issn.1007-130X.2018.01.012
基于复合优化的深度玻尔兹曼机的路牌文字图像识别算法
A traffic signs' Chinese character recognition algorithm based on mixed optimized deep Boltzmann machine
李文轩 1孙季丰1
作者信息
- 1. 华南理工大学电子与信息学院,广东广州510640
- 折叠
摘要
Abstract
In order to improve the recognition rate of traffic signs'Chinese characters,we propose a mixed optimized deep Boltzmann machine(MDBM) algorithm to improve the approximation of probability distribution.Two sampling methods (grayscale sampling initialization and binary sampling initialization) are proposed to construct the restricted Boltzmann machines,which are overlapped to form the depth Boltzmann machine.In addition,we propose a fine-tuning algorithm,called complex conjugate gradient method,to improve the fine-tuning part in deep Boltzmann machine.Experiments on traffic signs data show that the recognition rate of the proposed algorithm is better than that of the original deep Boltzmann machine.关键词
深度玻尔兹曼机/路牌/混合初始化/机器学习/文字识别Key words
deep Boltzmann machine/traffic signs/mixed initialization/machine learning/Chinese characters recognition分类
信息技术与安全科学引用本文复制引用
李文轩,孙季丰..基于复合优化的深度玻尔兹曼机的路牌文字图像识别算法[J].计算机工程与科学,2018,40(1):79-85,7.
