Classification and recognition of handwritten digits by using mathematical morphology

被引:8
|
作者
Vijaya kumar V. [1 ]
Srikrishna A. [2 ]
Babu B.R. [2 ]
Mani M.R. [3 ]
机构
[1] Department of Computer Science and Engineering and Information Technology, Godavari Institute of Engineering and Technology
[2] Department of Computer Science and Engineering and Information Technology, Rayapati Venkata Ranga Rao (RVR) and Jagarlamudi Chandramouli (JC) College of Engineering
[3] Department of Computer Science and Engineering, Godavari Institute of Engineering and Technology
关键词
blob(s); connected components; Region filling; stem(s); thinning;
D O I
10.1007/s12046-010-0031-z
中图分类号
学科分类号
摘要
The present paper proposes a novel algorithm for recognition of handwritten digits. For this, the present paper classified the digits into two groups: one group consists of blobs with/without stems and the other digits with stems only. The blobs are identified based on a new concept called morphological region filling methods. This eliminates the problem of finding the size of blobs and their structuring elements. The digits with blobs and stems are identified by a new concept called 'connected component'. This method completely eliminates the complex process of recognition of horizontal or vertical lines and the property called 'concavities'. The digits with only stems are recognized, by extending stems into blobs by using connected component approach of morphology. The present method has been applied and tested with various handwritten digits from modified NIST (National Institute of Standards and Technology) handwritten digit database (MNIST), and the success rate has been given. The present method is also compared with various existing methods. © 2010 Indian Academy of Sciences.
引用
收藏
页码:419 / 426
页数:7
相关论文
共 50 条
  • [21] Handwritten Digits Recognition Using HMM and PSO based on storks
    Yan, Liao
    Jia, Zhenhong
    Yang, Jie
    Pang, Shaoning
    2010 INTERNATIONAL CONFERENCE ON DISPLAY AND PHOTONICS, 2010, 7749
  • [22] RECOGNITION OF HANDWRITTEN DIGITS USING TEMPLATE AND MODEL-MATCHING
    GADER, P
    FORESTER, B
    GANZBERGER, M
    GILLIES, A
    MITCHELL, B
    WHALEN, M
    YOCUM, T
    PATTERN RECOGNITION, 1991, 24 (05) : 421 - 431
  • [23] Handwritten digits recognition using Hough transform and neural networks
    Castellano, G
    Sandler, MB
    ISCAS 96: 1996 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS - CIRCUITS AND SYSTEMS CONNECTING THE WORLD, VOL 3, 1996, : 313 - 316
  • [24] Recognition of degraded handwritten digits using dynamic Bayesian networks
    Likforman-Sulem, Laurence
    Sigelle, Marc
    DOCUMENT RECOGNITION AND RETRIEVAL XIV, 2007, 6500
  • [25] Recognition of Handwritten English and Digits Using Stroke Features and MLP
    Chen, Chung-Hsing
    Huang, Zih-Hao
    Huang, Ko-Wei
    2022 JOINT 12TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS AND 23RD INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS (SCIS&ISIS), 2022,
  • [26] Classification of Persian Handwritten Digits Using Spiking Neural Networks
    Kiani, Kourosh
    Korayem, Elmira Mohsenzadeh
    2015 2ND INTERNATIONAL CONFERENCE ON KNOWLEDGE-BASED ENGINEERING AND INNOVATION (KBEI), 2015, : 1113 - 1116
  • [27] A Novel Classification of Handwritten Digits Using Compressive Sensing Technique
    Tripathy, Soumya
    Panda, Ganapati
    2016 INTERNATIONAL CONFERENCE ON COMPUTATIONAL TECHNIQUES IN INFORMATION AND COMMUNICATION TECHNOLOGIES (ICCTICT), 2016,
  • [28] Classification of handwritten digits using a RAM neural net architecture
    Jorgensen, TM
    INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 1997, 8 (01) : 17 - 25
  • [29] An Effective and Improved CNN-ELM Classifier for Handwritten Digits Recognition and Classification
    Ali, Saqib
    Li, Jianqiang
    Pei, Yan
    Aslam, Muhammad Saqlain
    Shaukat, Zeeshan
    Azeem, Muhammad
    SYMMETRY-BASEL, 2020, 12 (10): : 1 - 15
  • [30] Ncfm: Accurate Handwritten Digits Recognition using Convolutional Neural Networks
    Yin, Yan
    Wu, JunMin
    Zheng, HuanXin
    2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2016, : 525 - 531