本文關鍵詞： 自動閱卷 圖像處理 條形碼識別 手寫字母識別 LVQ神經網絡　出處：《太原理工大學》2017年碩士論文　論文類型：學位論文

【摘要】：自動閱卷系統(tǒng)由于其高效的批閱處理、更為客觀公正的評分機制以及更加方便的管理功能等優(yōu)點正逐步替代著傳統(tǒng)的人工閱卷方式�，F在流行的自動閱卷系統(tǒng)多采用光標閱讀機實現答題卡的自動識別,這種方式對答題卡紙質要求較高,且需要購置昂貴的專用識別設備,比較適用于大型考試中。對于近年來出現的基于圖像處理的自動閱卷系統(tǒng)也是針對填涂模式的客觀題進行識別,這種模式下的識別率對考生的填涂質量依賴太強,容易造成系統(tǒng)誤判,而且也不符合考生的答題習慣,還會占用考生較多的填涂時間。針對填涂模式存在的問題,本文對基于手寫字母識別模式的自動閱卷系統(tǒng)進行研究。同時只針對試題與答案分離的答題紙進行處理,以減少掃描工作量,提高圖像處理的速度,節(jié)省系統(tǒng)運行時間與存儲空間的開銷。本文的主要研究內容如下:(1)結合答題紙圖像的特征簡化了傾斜校正的過程。對于Hough變換檢測直線的過程中計算量較大的問題,先對答題紙圖像的特征區(qū)域進行邊緣檢測,再對邊緣圖像中的橫線點進行篩選,最后進行Hough變換得到圖像的傾斜角度。(2)提出基于垂直投影的條形碼識別方法。將條形碼圖像識別技術引入到考生的信息識別過程中,簡化系統(tǒng)識別的過程,提高識別準確率�；诖怪蓖队暗臈l形碼識別方法,可以實現對受到嚴重污染和殘缺不全的條形碼圖像的快速準確地識別。(3)提出了一種手寫字母特征提取的新方法。針對傳統(tǒng)手寫字母特征提取方案獲得的特征點數較多,造成識別系統(tǒng)結構較為復雜的問題,結合手寫字母的特點,提出了八點特征提取方法。經過實驗測試證明,對基于八點特征提取法提取的特征點進行識別可以準確地辨識出其代表的字母,同時識別準確率也比較高。(4)基于八點特征提取方法,通過改進遺傳算法優(yōu)化的LVQ神經網絡,實現了手寫字母的自動識別。對于神經網絡因為初始權值設置不合理可能會出現“死”神經元的問題,加入了遺傳算法對其進行優(yōu)化。并對遺傳算法進行了改進,加快收斂速度,避免陷入局部最優(yōu)解。通過實驗測試證明,經過改進遺傳算法優(yōu)化的LVQ網絡的收斂性和分類性能都有明顯的改善和提升。同時基于八點特征提取法的LVQ神經網絡的網絡結構也比較簡單,對手寫字母的識別正確率也比較高,滿足了自動閱卷系統(tǒng)的性能要求。本文研究的技術和方法對解決基于圖像處理的自動閱卷系統(tǒng)的關鍵問題有很大的借鑒意義,適合應用在中小型考試的閱卷工作中。
[Abstract]:Automatic marking system due to its efficient marking processing. The advantages of more objective and fair scoring mechanism and more convenient management function are gradually replacing the traditional manual marking method. Now the popular automatic marking system uses the cursor reader to realize the automatic recognition of the answer card. . In this way, the paper requirement of the answer card is high, and expensive special identification equipment is needed. The automatic marking system based on image processing in recent years is also used to identify the objective problems of the filling pattern. The recognition rate in this mode is too dependent on the quality of the examinee's filling, and it is easy to cause system misjudgment, and it does not accord with the examinee's habit of answering questions. Also will occupy the examinee more filling time. In this paper, the automatic marking system based on the pattern of handwritten letter recognition is studied. At the same time, only the answer paper which is separated from the answer is processed, in order to reduce the scanning workload and improve the speed of image processing. The main contents of this paper are as follows: 1). The process of skew correction is simplified by combining the feature of the answer paper image. The problem of large computation in the process of detecting straight line by Hough transform is discussed. First, the feature region of the answer paper image is detected, and then the transverse points in the edge image are screened. Finally, Hough transform is carried out to get the tilt angle of the image.) A bar code recognition method based on vertical projection is proposed, and the bar code image recognition technology is introduced into the information recognition process of the examinee. The process of system recognition is simplified and the recognition accuracy is improved. The barcode recognition method based on vertical projection is presented. Can realize fast and accurate recognition of seriously polluted and incomplete barcode images. A new method for feature extraction of handwritten letters is proposed. Because of the complex structure of the recognition system, combined with the characteristics of the handwritten letters, an eight-point feature extraction method is proposed, which is proved by the experiment. Recognition of feature points based on eight-point feature extraction method can accurately identify its representative letters, and the recognition accuracy is also relatively high. 4) based on eight-point feature extraction method. By improving the LVQ neural network optimized by genetic algorithm, the automatic recognition of handwritten letters is realized. For the neural network, the problem of "dead" neurons may occur because the initial weights are not set properly. The genetic algorithm is added to optimize it, and the genetic algorithm is improved to speed up the convergence speed and avoid falling into the local optimal solution. The convergence and classification performance of the improved genetic algorithm (GA) optimized LVQ neural network are improved and improved obviously. At the same time, the network structure of the LVQ neural network based on the eight-point feature extraction method is also relatively simple. The recognition accuracy of handwritten letters is also high. Meet the performance requirements of automatic marking system. The techniques and methods studied in this paper have great reference significance to solve the key problems of automatic marking system based on image processing. Suitable for small and medium-sized examination paper marking work.
【學位授予單位】：太原理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

【參考文獻】

相關期刊論文 前10條

1 余濱杉;王社良;楊濤;樊禹江;;基于遺傳算法優(yōu)化的SMABP神經網絡本構模型[J];金屬學報;2017年02期

2 劉洋;黃欣;;基于圖像識別的網上閱卷系統(tǒng)的設計[J];電子技術與軟件工程;2016年19期

3 陳園園;袁煥麗;石齊雙;;基于神經網絡的手寫體數字識別[J];智能計算機與應用;2016年03期

4 馬利克;彭進業(yè);馮曉毅;;遺傳算法優(yōu)化LVQ網絡的監(jiān)控視頻關鍵幀內容識別[J];西北大學學報(自然科學版);2015年04期

5 周旭;伍東亮;;基于LVQ神經網絡算法的脫機手寫數字識別研究[J];湖南城市學院學報(自然科學版);2014年02期

6 王曉娟;白艷萍;;基于BP神經網絡的手寫體數字的識別方法研究[J];數學的實踐與認識;2014年07期

7 時恩早;;基于向量投影的KNN快速手寫阿拉伯數字識別[J];科技通報;2013年12期

8 張超;魏三強;胡秀建;梁西陳;;基于粒子群算法優(yōu)化LVQ神經網絡的應用研究[J];貴州大學學報(自然科學版);2013年05期

9 關保林;巴力登;;基于改進遺傳算法的BP神經網絡手寫數字識別[J];化工自動化及儀表;2013年06期

10 馬飛;呂海蓮;楊帥;程榮花;;基于圖像處理的客觀題自動閱卷系統(tǒng)研究開發(fā)[J];計算機技術與發(fā)展;2012年07期

相關碩士學位論文 前10條

1 王芳;基于XML的網上閱卷系統(tǒng)設計[D];太原理工大學;2016年

2 鄧凱;一種圖像識別手寫字符的自動閱卷系統(tǒng)[D];太原理工大學;2015年

3 劉志方;基于數字圖像處理的答題卡自動識別軟件[D];華南理工大學;2015年

4 陳浩鵬;基于圖像識別的移動端閱卷系統(tǒng)的研究與實現[D];中山大學;2014年

5 鄭婷;一種DOM與圖像工程相結合的智能閱卷系統(tǒng)[D];太原理工大學;2014年

6 周旭;基于LVQ神經網絡的脫機手寫數字識別研究[D];長沙理工大學;2013年

7 李清;基于圖像識別的網上閱卷系統(tǒng)的設計實現與優(yōu)化[D];東北師范大學;2013年

8 郭曉芳;基于小波變換的網上閱卷圖像傾斜校正方法[D];鄭州大學;2013年

9 陽軍;基于SVM分類設計的數碼閱卷圖像識別[D];寧波大學;2012年

10 董華冰;一維圖像條形碼識別方法研究[D];華南理工大學;2012年

，

本文編號：1486034