【正文】
ACT Today the science and technology develop rapidly. Letter recognition technology based on the feedback neural work is applied in many aspects including publication, finance military, cash register, page views, and any with repeatability,and variability of data files . Letter Identification System include the following processes: preprocessing, feature extraction, BP neural work training,and recognition.. In this paper, we use a threelayer neural work, including input layer, hidden layer and output layer. This paper supply of a variety of methods to determine Hidden layer nodes . The root sign method and other proposed by the Nelson and Illingwnrth are applied . The features and advantages of Artificial neural work is reflected in three aspects : First, a selflearning function. When we recognize letters, only putting many different images and the corresponding results into the artificial neural work and forming a stable weight before the letter recognition,the work will be through selflearning function to slowly identify similar , with the association storage. Artificial neural work feedback work can achieve this association in the letter recognition. Third, finding the optimal solution with high capacity. Finding the optimal solution of a plex often require a large amount of a design that a feedback type artificial neural work for problem and playing the highspeed puting power of puter, you may quickly find the optimal solution. In the matlab environment this article simulate the entire process of letter recognition, with the development of science and technology recognition technology is more mature and have various problems will be solved. Keywords: Letter identification; image processing; feature extraction; the feedback neural work 濟(jì)南大學(xué)泉城學(xué)院畢業(yè)論文 III 目 錄 摘 要 ........................................................................................................... 1 ABSTRACT .......................................................................................................... II 1 前言 .................................................................................................................. 1 研究背景及意義 ..................................................................................... 1 研究現(xiàn)狀 ................................................................................................. 2 手寫字母識別方法 ................................................................................. 3 結(jié)構(gòu)模式識別方法 .................................................................... 3 統(tǒng)計(jì)模式識別方法 .................................................................... 3 統(tǒng)計(jì)與結(jié)構(gòu)相結(jié)合的識別方法 ................................................ 4 人工神經(jīng)網(wǎng)絡(luò)方法 .................................................................... 4 識別系統(tǒng)性能的評價(jià) ............................................................................. 5 論文組織結(jié)構(gòu) ......................................................................................... 5 2 預(yù)處理 ................................................................................................................ 6 系統(tǒng)框架 ................................................................................................. 6 預(yù)處理概述 ............................................................................................. 6 本文預(yù)處理設(shè)計(jì) ..................................................................................... 6 去噪 ............................................................................................... 7 二值化 ........................................................................................... 8 歸一化 ...................................................................................... 10 細(xì)化 ............................................................................................. 11 3 字母特征提取 ................................................................................................ 13 特征提取概述 ....................................................................................... 13 本文特征提取設(shè)計(jì) ............................................................................... 13 像素百分比特征 ......................................................................... 14 提取矩陣的粗網(wǎng)格特征 ............................................................. 15 重心特征 .................................................................................... 16 提取圖像的矩陣像素特征 ........................................................ 16 筆劃特征 ..................................................................................... 17 外輪廓特征提取 ........................................................................ 18 4 BP 神經(jīng)網(wǎng)絡(luò) .................................................................................................. 19 人工神經(jīng)網(wǎng)絡(luò) ....................................................................................... 19 神經(jīng)網(wǎng)絡(luò)的模型圖 ................................................................................ 20 BP 神經(jīng)網(wǎng)絡(luò)的工作原理 ................................................................... 21 濟(jì)南大學(xué)泉城學(xué)院畢業(yè)論文 IV 神經(jīng)網(wǎng)絡(luò)的各層節(jié)點(diǎn)數(shù) ........................................................................ 22 輸入層和輸出層 ....................................................................... 22 隱含層節(jié)點(diǎn)數(shù)的優(yōu)化確定 ...................................................... 23 BP 神經(jīng)網(wǎng)絡(luò)的參數(shù)設(shè)計(jì)和訓(xùn)練過程 [17] ............................................. 25 5 實(shí)驗(yàn)結(jié)果及分析 .............................................................................................. 28 實(shí)驗(yàn)設(shè)計(jì) ............................................................................................... 28 實(shí)驗(yàn)參數(shù) .................................................................................... 28 訓(xùn)練和識別樣本庫設(shè)計(jì) ............................................................ 28 隱含層節(jié)點(diǎn)對實(shí)驗(yàn)結(jié)果的影響 ............................................................ 28 識別樣本的正確率 ...............................