A modified current mode Hamming neural network for totally unconstrained handwritten numeral recognition

被引：0
|
作者：
Li, GX [1 ]
Shi, BX [1 ]
Lu, W [1 ]
机构：
[1] Tsing Hua Univ, Inst Microelect, Beijing 100084, Peoples R China
来源：
IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE | 1998年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A compact smart current mode Hamming neural network for classifying complex pattern such as totally unconstrained handwritten digit is presented in this paper. It is based on multi-threshold template matching, multi-stage matching and k-WTA(k-Winner-Taker-All), some different from general Hamming neural network. The neural classifier consists of two kinds of templates : one is binary template and another is multi-value programmable templates, each of them has its own threshold and realized in MOS current mirrors, the current mode k-WTA which is reconfigurable is put forward, The second stage matching templates are programmable from outside of the chip. This mixed analog-digital Hamming neural classifier can be fabricated in a standard digital CMOS technology.
引用
收藏
页码：1857 / 1860
页数：4
相关论文
共 50 条
  • [41] Hybrid evolutionary approach for Devanagari handwritten numeral recognition using Convolutional Neural Network
    Trivedi, Adarsh
    Srivastava, Siddhant
    Mishra, Apoorva
    Shukla, Anupam
    Tiwari, Ritu
    [J]. 6TH INTERNATIONAL CONFERENCE ON SMART COMPUTING AND COMMUNICATIONS, 2018, 125 : 525 - 532
  • [42] Recognition of unconstrained handwritten numerals by a radial basis function neural network classifier
    Hwang, YS
    Bang, SY
    [J]. PATTERN RECOGNITION LETTERS, 1997, 18 (07) : 657 - 664
  • [43] Handwritten Numeral Recognition Using Modified BP ANN Structure
    Choudhary, Amit
    Rishi, Rahul
    Ahlawat, Savita
    [J]. ADVANCED COMPUTING, PT III, 2011, 133 : 56 - +
  • [44] Recognition of Isolated Handwritten Persian Characterizing Hamming Network
    Arabfard, Masoud
    Askari, Meisam
    Asadi, Milad
    Ebrahimpour-Komleh, Hosein
    [J]. INNOVATIVE COMPUTING TECHNOLOGY, 2011, 241 : 293 - 304
  • [45] Reliable Recognition of Handwritten Digits Using Hamming Network
    Archana, S.
    Madhavi, B. K.
    Krishna, I. V. Murali
    [J]. 2016 IEEE 6TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING (IACC), 2016, : 476 - 479
  • [46] Handwritten Bangla Numeral Recognition using Convolutional Neural Networks
    Paul, Jaya
    Sarkar, Anasua
    [J]. 2018 2ND INTERNATIONAL CONFERENCE ON ELECTRONICS, MATERIALS ENGINEERING & NANO-TECHNOLOGY (IEMENTECH), 2018, : 64 - 67
  • [47] Handwritten numeral and machine printed multiple font character recognition using neural network classifier
    Cao, J
    Ahmadi, M
    Shridhar, M
    [J]. JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS, 1996, 6 (06) : 569 - 580
  • [48] Devanagari Offline Handwritten Numeral and Character Recognition using Multiple Features and Neural Network Classifier
    Dongre, Vikas J.
    Mankar, Vijay H.
    [J]. 2015 2ND INTERNATIONAL CONFERENCE ON COMPUTING FOR SUSTAINABLE GLOBAL DEVELOPMENT (INDIACOM), 2015, : 425 - 431
  • [49] Off-line Odia Handwritten Numeral Recognition Using Neural Network: A Comparative Analysis
    Sethy, Abhisek
    Patra, Prashanta Kumar
    [J]. 2016 IEEE INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND AUTOMATION (ICCCA), 2016, : 1099 - 1103
  • [50] A segmentation-free recognition of handwritten touching numeral pairs using modular neural network
    Choi, SM
    Oh, IS
    [J]. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2001, 15 (06) : 949 - 966
12345