本文選題：識別技術 + 面值識別�。� 參考：《遼寧科技大學》2016年碩士論文

【摘要】：隨著科技的迅猛發(fā)展和社會的不斷進步,現(xiàn)如今識別技術正以驚人的速度發(fā)展。識別技術是一個包涵了圖像識別技術、指紋識別技術、人臉識別技術、自動識別技術等為一體的現(xiàn)代新型技術。識別技術產(chǎn)業(yè)具有不可估計的發(fā)展前景,其中數(shù)字號碼識別技術的發(fā)展尤為迅速。隨著現(xiàn)代金融業(yè)電子化的發(fā)展,人民幣面值快速識別技術依舊在銀行電子化業(yè)務系統(tǒng)中扮演著重要角色。本文在查閱國內外參考文獻的基礎上,針對紙幣圖像的采集、紙幣圖像的預處理、紙幣圖像的面值識別、紙幣圖像的面向識別以及紙幣圖像的新舊識別設計了人民幣面值快速識別系統(tǒng)。經(jīng)仿真實驗證明,基本滿足了對紙幣快速識別的要求。本文的核心研究內容為對經(jīng)過采集和轉化后的紙幣面值進行圖像預處理和識別。其中包括去圖像去噪處理,中值濾波,圖像的傾斜校正等幾部分,然后在對國內傳統(tǒng)紙幣面值識別方法基礎上做一些改進,以第四版與第五版人民幣為研究對像,采用圖像處理技術與模式識別技術相結合的方法完成對紙幣面值的識別,提高了面值識別的準確率,并針對紙幣面向識別采用了SOFM神經(jīng)網(wǎng)絡識別技術,照比傳統(tǒng)的識別技術做出了一定的改進,在紙幣新舊識別方面本文將國內常用識別方法HIS方法、BP-LVQ方法、LVQ方法進行比較研究。實驗結果驗證了人民幣面值快速識別算法并針對以往算法有了明顯改進,經(jīng)過分析比較新舊識別三種方法方法在不同背景下具有不同優(yōu)點,能滿足現(xiàn)代金融業(yè)對紙幣識別的要求,具有一定的價值。
[Abstract]:With the rapid development of science and technology and social progress, recognition technology is developing at an alarming speed. Recognition technology is a new modern technology which includes image identification technology, fingerprint recognition technology, face recognition technology, automatic identification technology and so on. Recognition technology industry has an inestimable development prospect, especially digital number recognition technology. With the development of modern financial industry, RMB face value recognition technology still plays an important role in the electronic banking business system. On the basis of consulting references at home and abroad, this paper aims at the collection of banknote images, the preprocessing of banknote images, and the recognition of the face value of banknote images. A fast recognition system of RMB face value is designed for paper currency image and the new and old recognition of paper currency image. The simulation results show that it can meet the requirement of paper currency recognition. The core of this paper is image preprocessing and recognition of the collected and converted banknotes. This includes image denoising, median filtering, image skew correction, and so on. Then some improvements are made on the basis of the traditional method of recognizing the face value of domestic banknotes. The fourth and fifth editions of RMB are taken as the research objects. The recognition of banknote face value is accomplished by combining image processing technology with pattern recognition technology, and the accuracy of face value recognition is improved, and the SOFM neural network recognition technology is used for banknote face recognition. Compared with the traditional recognition technology, this paper compares his method with BP-LVQ method and LVQ method in the recognition of new and old banknotes. The experimental results verify the fast recognition algorithm of RMB face value and improve obviously the previous algorithms. After analyzing and comparing the new and old recognition methods, the three methods have different advantages in different backgrounds. To meet the requirements of the modern financial industry for paper money recognition, has a certain value.
【學位授予單位】：遼寧科技大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41

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本文編號：2018781