Fast identification method for express end sorting label code based on convolutional recurrent neural network

被引:0
|
作者
Haiyan Du
Chunxue Wu
Yan Wu
Ren Han
Xiao Lin
Sheng Zhang
机构
[1] University of Shanghai for Science and Technology,School of Optical
[2] Indiana University,Electrical and Computer Engineering
[3] Shanghai Normal University,The School of Public and Environmental Affairs
来源
Signal, Image and Video Processing | 2020年 / 14卷
关键词
Express end; Code positioning; Label code recognition; Sequence recognition; Convolutional recurrent neural network;
D O I
暂无
中图分类号
学科分类号
摘要
In the automatic sorting process of express, the express end sorting label code is used to indicate that the express is dispatched to a specific address by a specific courier. Since there are many areas on the express bill containing digital information, some areas may be improperly photographed, etc. The difficulty in positioning and recognizing the express end sorting label code region is increased. To solve this problem, this paper proposes an express end sorting label code recognition method with convolutional recurrent neural network for the code specification, which has certain versatility. In order to improve the overall code recognition speed, this paper optimizes the traditional digital recognition method, removes the original segmentation operation of the character and recognizes the code as sequence recognition. Firstly, the coding region is located, and then, the express end sorting label code is recognized by the convolutional recurrent neural network. In order to test the experimental performance, this paper tests on Free-Type dataset and SUN-synthesized dataset. The experimental results show that the proposed method improves the recognition accuracy and processing speed of the express end sorting label code.
引用
收藏
页码:1689 / 1697
页数:8
相关论文
共 50 条
  • [1] Fast identification method for express end sorting label code based on convolutional recurrent neural network
    Du, Haiyan
    Wu, Chunxue
    Wu, Yan
    Han, Ren
    Lin, Xiao
    Zhang, Sheng
    SIGNAL IMAGE AND VIDEO PROCESSING, 2020, 14 (08) : 1689 - 1697
  • [2] Identification Method of Strawberry Based on Convolutional Neural Network
    Liu X.
    Fan C.
    Li J.
    Gao Y.
    Zhang Y.
    Yang Q.
    Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, 2020, 51 (02): : 237 - 244
  • [3] Study on a Recurrent Convolutional Neural Network Based FDTD Method
    Guo, Liangshuai
    Li, Maokun
    Xu, Shenheng
    Yang, Fan
    2019 INTERNATIONAL APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY SYMPOSIUM - CHINA (ACES), VOL 1, 2019,
  • [4] Identification and Classification of Electrocardiogram Signals Based On Convolutional Recurrent Neural Network
    Ma, Jinwei
    Liu, Shengping
    Chen, Guoming
    2018 11TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2018), 2018,
  • [5] Snoring identification method based on residual convolutional neural network
    Shin, Seung-Su
    Kim, Hyoung-Gook
    JOURNAL OF THE ACOUSTICAL SOCIETY OF KOREA, 2019, 38 (05): : 574 - 579
  • [6] Speech enhancement method based on convolutional gated recurrent neural network
    Yuan W.
    Lou Y.
    Xia B.
    Sun W.
    Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition), 2019, 47 (04): : 13 - 18
  • [7] Sunflower Seed Sorting Based on Convolutional Neural Network
    Luan, Zhengguang
    Li, Chunlei
    Ding, Shumin
    Wei, Miaomiao
    Yang, Yan
    ELEVENTH INTERNATIONAL CONFERENCE ON GRAPHICS AND IMAGE PROCESSING (ICGIP 2019), 2020, 11373
  • [8] Fast Identification of Field Weeds Based on Deep Convolutional Network and Binary Hash Code
    Jiang H.
    Wang P.
    Zhang Z.
    Mao W.
    Zhao B.
    Qi P.
    Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, 2018, 49 (11): : 30 - 38
  • [9] Study on Express Parcels Classification Encoding Method based on Deep Convolutional Neural Network
    Wang, Linlin
    INTERNATIONAL JOURNAL OF MULTIPHYSICS, 2024, 18 (03) : 330 - 337
  • [10] A Fast and Robust Gas Recognition Algorithm Based on Hybrid Convolutional and Recurrent Neural Network
    Pan, Xiaofang
    Zhang, Haien
    Ye, Wenbin
    Bermak, Amine
    Zhao, Xiaojin
    IEEE ACCESS, 2019, 7 : 100954 - 100963