中南民族大学学报（自然科学版）2024，Vol.43Issue(3)：393-400,8.DOI:10.20056/j.cnki.ZNMDZK.20240314
基于胶囊网络和语言模型的政务文字识别
Character recognition for government affairs based on capsule network and language model
摘要
Abstract
Character recognition is one of the important research contents in the field of computer vision,which lays the foundation for building intelligent government services.However,the uneven quality of government images and diverse font styles cause the low recognition accuracy.In order to solve above problems,a CNLM model combining capsule network and language model is proposed,and the character cutting is combined with capsule network.Firstly,the government image dataset is constructed as character recognition images and sentence samples of the language model for training in stages,in the first stage,the visual model is pre-trained by public character cut dataset,and the language model is pre-trained by sentence samples and existing structured data.In the second stage,the visual model and language model are jointly trained,the output results of them are selected and iterated to finally obtain the text sequence information contained in the images.The method is tested on both the government image dataset and GA-HWDB dataset,and its accuracy is improved by 2.12%and 2.69%compared with VisionLAN.关键词
智能政务/文字识别/胶囊网络/语言模型Key words
intelligent government affair/character recognition/capsule network/language model分类
信息技术与安全科学引用本文复制引用
于龙洋,王德军,孟博,吴余龙,胡宗华,段伟..基于胶囊网络和语言模型的政务文字识别[J].中南民族大学学报（自然科学版）,2024,43(3):393-400,8.基金项目
湖北省科技创新人才计划资助项目(2023DJC094) （2023DJC094）
国家重点研发计划资助项目(2020YFC1522900) （2020YFC1522900）
中南民族大学研究生学术创新基金资助项目(3212023sycxjj168) （3212023sycxjj168）
