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研究生:郭癸蘭
研究生(外文):Kuei-Lan Kuo
論文名稱:手寫身分證字號辨識系統
論文名稱(外文):Handwritten ID Number Recognition System
指導教授:周義昌
指導教授(外文):I-Chang Jou
學位類別:碩士
校院名稱:國立高雄第一科技大學
系所名稱:電腦與通訊工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:89
中文關鍵詞:類神經網路手寫字辨識
外文關鍵詞:Neural Networkrecognition
相關次數:
  • 被引用被引用:21
  • 點閱點閱:1155
  • 評分評分:
  • 下載下載:325
  • 收藏至我的研究室書目清單書目收藏:0
本篇論文將提出運用可塑性認知網路(Plastic Perception Neural Network)來完成手寫身分證字號辨識系統。文中所運用的可塑性認知網路的架構是針對類神經網路(Artificial Neural Networks)中倒傳遞類神經網路(Back-propagation Neural Network) 的學習法則以及其網路架構做改進,來克服傳統的倒傳遞類神經網路在訓練時學習速度太慢、不易收斂、及當刪除或加入新的訓練樣本(patterns)時必須全盤重新訓練等問題。這些缺點成為實現即時的倒傳遞類神經網路系統的嚴重障礙。為了克服這樣的障礙,我們結合平行分散處理的觀念,對倒傳遞類神經網路的網路架構做一改進,以增快其學習速度,並克服全盤重新訓練的問題。本文尚包括文字切割、雜訊去除,以及字型特徵值的抽取,適切的字型特徵值抽取有助於字型的辨識,本文所採用的特徵向量是結合白色跑長碼(White Run-Length)與圖素密度(Pixel Density),可以分別表現出字型的結構性與整體性,以達到高準確的辨識率。
This thesis brings up the implementation of handwritten ID number recognition system by the application of plastic perceptron neural network(PPNN). The applied structure of PPNN in this thesis is improved from the learning algorithm and network structure of back-propagation neural network (BPNN)in artificial neural networks. The problems of traditional BPNN such as longer learning period, not prone to convergence, re-training while delete or add new patterns make the realization of real time BPNN system impossible. The proposed methods are combined with the parallel distributive process concept and modification of the BPNN structure could accelerate the learning speed and solve the re-training problem. The character segmentation, noise removal and extraction of feature are also discussed. Adequate extracted feature make recognition of character easier. The adoption of white run-length and pixel density could clearly display the structural and integral of the character respectively, and facilitate to make higher recognition accuracy.
目 錄
                      
中文摘要 i
英文摘要 ii
誌謝 iv
目錄 v
圖目錄 vii
壹、緒論 1
一、 研究動機 1
二、 相關技術背景 1
三、 簡介 3
四、 本論文的組織 4
貳、 類神經網路原理的探討 5
一、 類神經元的模型 7
二、 類神經網路的架構 11
三、 多層感知網路與倒傳遞演算法 12
四、 類神經網路的學習規則的分析 18
五、 可塑性認知網路 19
參、手寫身分證字號辨識系統的架構 21
一、 前處理 22
二、 特徵抽取 26
三、 可塑性認知網路部分 29
肆、 系統的實作 33
一、 系統運作的流程 33
二、 樣本收集與前處理 33
三、 特徵向量抽取 38
四、 可塑性認知網路的訓練學習 43
伍、模擬結果與討論 46
一、 模擬結果 46
二、 討論 58
陸、結論與未來研究的方向 61
一、 結論 61
二、 未來研究的方向 61
參考文獻 63
附錄一 訓練程式 67
辨識程式 75
參考文獻
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[10] Hsin-Chia Fu, Hung-Yuan Chang, Yeong Yuh Xu, and H-T Pao, “User Adaptive Handwriting Recognition by Self-Growing Probabilistic Decision-Based Neural Networks”, IEEE Transactions on Neural Networks, Vol. 11, No.6,pp.1373-1384, November 2000.
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