本文選題：證件識(shí)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(xiě)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(jì)算機(jī),或者是,到指定地點(diǎn)進(jìn)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(diǎn),但每一次信息的錄入都需要耗費(fèi)大量的人力物力資源,并且容易出現(xiàn)錯(cuò)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(diǎn)卻相對(duì)固定。移動(dòng)智能終端的出現(xiàn),使隨時(shí)隨地進(jìn)行證件信息的錄入成為可能。移動(dòng)智能終端上的信息識(shí)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(duì)證件信息進(jìn)行查驗(yàn)的部分,無(wú)需大量人員即可完成證件信息的采集查驗(yàn),提高采集查驗(yàn)工作中證件信息識(shí)別的效率和準(zhǔn)確率,具有廣闊的應(yīng)用前景。如何對(duì)不同證件中的文字信息進(jìn)行良好的提取和識(shí)別,是開(kāi)發(fā)證件信息識(shí)別系統(tǒng)的關(guān)鍵問(wèn)題。識(shí)別一個(gè)證件圖像的關(guān)鍵信息,首要任務(wù)是對(duì)其關(guān)鍵信息進(jìn)行正確提取。本文針對(duì)不同證件,設(shè)計(jì)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(huà)寬度逼近的二值化方法,對(duì)圖像進(jìn)行二值化,減少圖像中背景、污點(diǎn)、反光等造成的影響,有效提升信息的識(shí)別率。本文在信息識(shí)別方面根據(jù)不同字符特點(diǎn),采用了兩種目前較為流行的方法對(duì)文字進(jìn)行識(shí)別。針對(duì)英文數(shù)字,本文采用Tesseract-OCR引擎進(jìn)行識(shí)別。英文數(shù)字字符結(jié)構(gòu)簡(jiǎn)單,類(lèi)別較少,使用Tesseract引擎的識(shí)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(dòng)智能終端的使用要求。針對(duì)中文漢字,漢字結(jié)構(gòu)復(fù)雜且種類(lèi)眾多,使用Tesseract引擎識(shí)別率不高,且生成語(yǔ)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(shí)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(bǔ)了單獨(dú)使用神經(jīng)網(wǎng)絡(luò)訓(xùn)練的過(guò)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(guò)程中的過(guò)擬合現(xiàn)象,提高最終模型對(duì)于文字的識(shí)別性能。該方法提升了文字的識(shí)別率,且生成模型較小,文字識(shí)別速度較快,便于移植到移動(dòng)智能終端。本文針對(duì)以上需求,開(kāi)發(fā)了一款移動(dòng)智能終端的證件信息識(shí)別系統(tǒng),目前主要支持識(shí)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(jī)。該系統(tǒng)能成功識(shí)別證件上的英文、數(shù)字、中文,英文數(shù)字識(shí)別率在98.4%左右,身份證號(hào)碼識(shí)別率達(dá)到99.2%左右,中文識(shí)別率達(dá)到98.27%左右,證件整體識(shí)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(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP391.41

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