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  本文選題：收件人地址塊定位 + BING�。� 參考：《華東師范大學》2017年碩士論文

【摘要】：郵件圖像的收件人地址塊定位作為郵政自動化的第一步,是實現(xiàn)自動分揀的先決條件。如何定位郵件圖像的收件人地址塊變成一個亟待解決的問題。尤其是塑封郵件的背景復雜,使得針對塑封郵件圖像的收件人地址塊定位具有很大挑戰(zhàn)性。本文提出了一種塑封郵件圖像的收件人地址塊定位方法,研究了候選域提取、特征表示和特征匹配的方法。本文的主要工作包括:1.提出基于改進BING模型的候選域提取方法。通過刻畫塑封郵件圖像中某塊區(qū)域的目標顯著性水平,自適應的產生一些高質量的收件人地址塊候選域。該方法克服了滑動窗口法無法適應收件人地址塊尺寸變化的缺點,能夠減少候選域的數(shù)目,降低塑封郵件圖像的搜索空間。2.提出采用稠密SIFT描述子對候選域進行特征描述。在詞袋模型的基礎上引入金字塔匹配原理,將候選域進行層級網(wǎng)格劃分,逐層基于視覺詞典對候選域的SIFT特征進行重新表示。該方法考慮了候選域中的視覺單詞在不同空間位置上的分布情況,保留了候選域的空間布局信息,是對詞袋模型的一種有效擴展。3.提出使用直方圖交叉核SVM對提取的特征進行匹配。對候選域提取基于視覺詞典的金字塔視覺直方圖,將直方圖作為特征向量輸入到訓練好的SVM模型中,得到每個候選域的概率。因為單個候選域無法完全覆蓋收件人地址塊,并且候選域與候選域之間有重疊,所以合并概率最高的前五個候選域,為最終提取收件人地址塊的文字區(qū)域提供基礎。我們在上海郵政科學研究院提供的塑封郵件圖像庫上進行了實驗,并對本文方法和對比方法的數(shù)據(jù)進行了分析。實驗數(shù)據(jù)顯示本文方法取得了較好的結果,可以有效定位出塑封郵件圖像的收件人地址塊。
[Abstract]:As the first step of postal automation, the location of recipient address block of mail image is the precondition of automatic sorting. How to locate the recipient address block of mail image becomes an urgent problem. Especially, the background of plastic mail is very complicated, which makes it challenging to locate the recipient address block of plastic mail image. In this paper, a method of location of addressee address block in plastic mail image is proposed, and the methods of candidate domain extraction, feature representation and feature matching are studied. The main work of this paper includes: 1. A candidate domain extraction method based on improved BING model is proposed. By characterizing the target significance level of a certain area in a plastic mail image, some candidate domains of high quality addressee address blocks are generated adaptively. This method overcomes the shortcoming that the sliding window method can not adapt to the size change of addressee address block and can reduce the number of candidate fields and the search space of plastic mail image. A dense SIFT descriptor is proposed to describe candidate domains. Based on the lexical bag model, the pyramid matching principle is introduced, the candidate fields are divided into hierarchical grids, and the SIFT features of the candidate domains are re-represented layer by layer based on visual dictionaries. This method takes into account the distribution of visual words in candidate domains in different spatial locations and retains the spatial layout information of candidate domains. It is an effective extension of the word bag model. A histogram cross kernel SVM is proposed to match the extracted features. The pyramid visual histogram based on visual dictionary is extracted from candidate domain and the histogram is input into the trained SVM model as feature vector to obtain the probability of each candidate domain. Because a single candidate domain can not completely cover the addressee address block and there is overlap between the candidate domain and the candidate domain, the first five candidate domains with the highest merging probability provide the basis for the final extraction of the text area of the recipient address block. We have carried out experiments on the plastic mail image library provided by Shanghai Postal Science Research Institute, and analyzed the data of our method and comparison method. The experimental data show that the proposed method can effectively locate the recipient address block of the plastic mail image.
【學位授予單位】：華東師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F250;TP391.41

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本文編號：1796591