本文選題：識別技術(shù) + 面值識別��； 參考：《遼寧科技大學(xué)》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(jìn)步,現(xiàn)如今識別技術(shù)正以驚人的速度發(fā)展。識別技術(shù)是一個(gè)包涵了圖像識別技術(shù)、指紋識別技術(shù)、人臉識別技術(shù)、自動識別技術(shù)等為一體的現(xiàn)代新型技術(shù)。識別技術(shù)產(chǎn)業(yè)具有不可估計(jì)的發(fā)展前景,其中數(shù)字號碼識別技術(shù)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(shù)依舊在銀行電子化業(yè)務(wù)系統(tǒng)中扮演著重要角色。本文在查閱國內(nèi)外參考文獻(xiàn)的基礎(chǔ)上,針對紙幣圖像的采集、紙幣圖像的預(yù)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(shè)計(jì)了人民幣面值快速識別系統(tǒng)。經(jīng)仿真實(shí)驗(yàn)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(nèi)容為對經(jīng)過采集和轉(zhuǎn)化后的紙幣面值進(jìn)行圖像預(yù)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(nèi)傳統(tǒng)紙幣面值識別方法基礎(chǔ)上做一些改進(jìn),以第四版與第五版人民幣為研究對像,采用圖像處理技術(shù)與模式識別技術(shù)相結(jié)合的方法完成對紙幣面值的識別,提高了面值識別的準(zhǔn)確率,并針對紙幣面向識別采用了SOFM神經(jīng)網(wǎng)絡(luò)識別技術(shù),照比傳統(tǒng)的識別技術(shù)做出了一定的改進(jìn),在紙幣新舊識別方面本文將國內(nèi)常用識別方法HIS方法、BP-LVQ方法、LVQ方法進(jìn)行比較研究。實(shí)驗(yàn)結(jié)果驗(yàn)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(jìn),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(diǎn),能滿足現(xiàn)代金融業(yè)對紙幣識別的要求,具有一定的價(jià)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(xué)位授予單位】：遼寧科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.41

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