本文選題：證件識別 + 圖像處理　； 參考：《武漢工程大學(xué)》2016年碩士論文

【摘要】：傳統(tǒng)的信息錄入方式是采用人工方式先填寫相關(guān)表格中信息,再由內(nèi)部工作人員按照表格內(nèi)容把關(guān)鍵信息存入計算機,或者是,到指定地點進行證件的掃描上傳。前一種方式雖然不限制信息錄入的地點,但每一次信息的錄入都需要耗費大量的人力物力資源,并且容易出現(xiàn)錯誤的輸入。后一種,雖然在信息錄入的效率和準(zhǔn)確率上都有提高,但是使用地點卻相對固定。移動智能終端的出現(xiàn),使隨時隨地進行證件信息的錄入成為可能。移動智能終端上的信息識別系統(tǒng)可以廣泛的應(yīng)用于服務(wù)性行業(yè)、交通系統(tǒng)、公安系統(tǒng)等需要對證件信息進行查驗的部分,無需大量人員即可完成證件信息的采集查驗,提高采集查驗工作中證件信息識別的效率和準(zhǔn)確率,具有廣闊的應(yīng)用前景。如何對不同證件中的文字信息進行良好的提取和識別,是開發(fā)證件信息識別系統(tǒng)的關(guān)鍵問題。識別一個證件圖像的關(guān)鍵信息,首要任務(wù)是對其關(guān)鍵信息進行正確提取。本文針對不同證件,設(shè)計了不同的圖像預(yù)處理方法,以確保證件信息能正確提取。本文采用一種字符筆畫寬度逼近的二值化方法,對圖像進行二值化,減少圖像中背景、污點、反光等造成的影響,有效提升信息的識別率。本文在信息識別方面根據(jù)不同字符特點,采用了兩種目前較為流行的方法對文字進行識別。針對英文數(shù)字,本文采用Tesseract-OCR引擎進行識別。英文數(shù)字字符結(jié)構(gòu)簡單,類別較少,使用Tesseract引擎的識別率已滿足本文系統(tǒng)需要,且生成的字符集體積小,滿足移動智能終端的使用要求。針對中文漢字,漢字結(jié)構(gòu)復(fù)雜且種類眾多,使用Tesseract引擎識別率不高,且生成語言體積較大,本文使用一種基于特征提取和卷積神經(jīng)網(wǎng)絡(luò)的漢字識別方法,將傳統(tǒng)特征提取方法與神經(jīng)網(wǎng)絡(luò)結(jié)合,彌補了單獨使用神經(jīng)網(wǎng)絡(luò)訓(xùn)練的過程中丟失的特征信息,并在其每一層使用Dropout技術(shù),有效預(yù)防神經(jīng)網(wǎng)絡(luò)在訓(xùn)練過程中的過擬合現(xiàn)象,提高最終模型對于文字的識別性能。該方法提升了文字的識別率,且生成模型較小,文字識別速度較快,便于移植到移動智能終端。本文針對以上需求,開發(fā)了一款移動智能終端的證件信息識別系統(tǒng),目前主要支持識別身份證正反面以及行駛證。該系統(tǒng)分為安卓版本和iOS版本,支持市面上絕大多數(shù)手機。該系統(tǒng)能成功識別證件上的英文、數(shù)字、中文,英文數(shù)字識別率在98.4%左右,身份證號碼識別率達到99.2%左右,中文識別率達到98.27%左右,證件整體識別率大約為90%。
[Abstract]:The traditional way of information input is to fill in the information in the relevant forms manually, and then the internal staff store the key information into the computer according to the contents of the form, or to the designated place to scan and upload the documents. Although the former method does not limit the location of information input, it requires a lot of human and material resources for each input, and it is prone to the wrong input. Although the efficiency and accuracy of information entry are improved, the location of the latter is relatively fixed. The appearance of mobile intelligent terminal makes it possible to input document information anytime and anywhere. The information identification system on the mobile intelligent terminal can be widely used in the service industry, transportation system, public security system and other parts that need to check the document information, and can complete the document information collection and inspection without a large number of personnel. It has broad application prospect to improve the efficiency and accuracy of document information recognition in collecting and checking work. How to extract and recognize the text information in different documents is a key problem in the development of document information recognition system. To identify the key information of a document image, the most important task is to extract the key information correctly. In this paper, different image preprocessing methods are designed for different documents to ensure that document information can be extracted correctly. In this paper, a binarization method of approaching the width of character strokes is used to binarize the image to reduce the influence caused by background, stain and reflection in the image, and to improve the recognition rate of the information effectively. In this paper, according to the characteristics of different characters, two popular methods are used to recognize characters in information recognition. In this paper, Tesseract-OCR engine is used to recognize English numbers. English numeric characters have simple structure and few categories. The recognition rate of Tesseract engine has met the needs of the system in this paper. The generated character set is small in size and meets the requirements of mobile intelligent terminal. In view of Chinese characters, the structure of Chinese characters is complex and there are many kinds of Chinese characters, the recognition rate of Tesseract engine is not high, and the volume of generated language is large. In this paper, a Chinese character recognition method based on feature extraction and convolution neural network is used. The traditional feature extraction method is combined with the neural network to make up for the missing feature information in the process of training using the neural network alone, and Dropout technology is used in each layer to effectively prevent the phenomenon of over-fitting in the training process of the neural network. Improve the performance of the final model for text recognition. The method improves the recognition rate of characters, and the generated model is smaller, and the recognition speed of characters is faster, so it is convenient to transplant to mobile intelligent terminal. In order to meet the above requirements, a mobile intelligent terminal identification system is developed in this paper. At present, it mainly supports the identification of the positive and negative sides of the ID card as well as the driving card. The system is divided into Android and iOS versions, supporting the vast majority of mobile phones on the market. The system can successfully identify the English, Chinese, Chinese and English numbers on the documents, the recognition rate of the ID numbers is about 98.4%, the identification rate of the ID numbers is about 99.2%, the recognition rate of the Chinese characters is about 98.27%, and the overall identification rate of the documents is about 90%.
【學(xué)位授予單位】：武漢工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.41

【參考文獻】

相關(guān)期刊論文 前10條

1 陳梓洋;王宇飛;錢侃;張超;孫知信;;自然場景下基于區(qū)域檢測的文字識別算法[J];計算機技術(shù)與發(fā)展;2015年07期

2 劉新瀚;錢侃;王宇飛;朱向霄;孫知信;;自然場景下基于連通域檢測的文字識別算法研究[J];計算機技術(shù)與發(fā)展;2015年05期

3 李旋;;圖像文本定位算法的研究與設(shè)計[J];信息系統(tǒng)工程;2015年02期

4 潘煒深;金連文;馮子勇;;基于多尺度梯度及深度神經(jīng)網(wǎng)絡(luò)的漢字識別[J];北京航空航天大學(xué)學(xué)報;2015年04期

5 林孜陽;穆雪;吳凱鋒;嚴(yán)寒;林怡芳;;基于連通域的快速文字圖像分割算法[J];計算機光盤軟件與應(yīng)用;2014年22期

6 付輝;呂磊;茍芳;付強;;邊緣檢測算法分析與實現(xiàn)[J];科技傳播;2014年21期

7 李良旭;孫高祥;張哲;;一種光照不均文本圖像的校正算法[J];電腦與信息技術(shù);2014年01期

8 褚晶輝;董越;呂衛(wèi);;基于小波變換的文字檢測與提取方法[J];電視技術(shù);2014年03期

9 孫華;李愛平;;支持向量機的古漢字識別研究[J];電腦知識與技術(shù);2013年18期

10 郭亞;黃艷國;許倫輝;;基于形態(tài)學(xué)和梯度重構(gòu)的車牌圖像分割方法[J];計算機應(yīng)用研究;2011年11期

，

本文編號：2037493