WO2007011188A1 - Procede et appareil pour reconnaitre un numero de serie de papier-monnaie - Google Patents

Procede et appareil pour reconnaitre un numero de serie de papier-monnaie Download PDF

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Publication number
WO2007011188A1
WO2007011188A1 PCT/KR2006/002879 KR2006002879W WO2007011188A1 WO 2007011188 A1 WO2007011188 A1 WO 2007011188A1 KR 2006002879 W KR2006002879 W KR 2006002879W WO 2007011188 A1 WO2007011188 A1 WO 2007011188A1
Authority
WO
WIPO (PCT)
Prior art keywords
character
image
paper money
characters
data
Prior art date
Application number
PCT/KR2006/002879
Other languages
English (en)
Inventor
Jae-Huan Park
Sang-Youl Jeon
Sang-Keun Seo
Seung-Hwan Seo
Gy-Yeop Kim
Original Assignee
Seetech Co., Ltd.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Seetech Co., Ltd. filed Critical Seetech Co., Ltd.
Publication of WO2007011188A1 publication Critical patent/WO2007011188A1/fr

Links

Classifications

    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07D—HANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
    • G07D7/00—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency
    • G07D7/003—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency using security elements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10—Character recognition
    • G06V30/12—Detection or correction of errors, e.g. by rescanning the pattern
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10—Character recognition
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07D—HANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
    • G07D2207/00—Paper-money testing devices

Definitions

  • the present invention relates to a method and an apparatus for recognizing a serial number of paper money, and more particularly, to a method and an apparatus for extracting and processing an image of paper money in a paper money counter or a forged paper money discriminator to recognize a serial number of the paper money.
  • Paper money counters are used in banks or the like to count a number of paper money.
  • General paper money counters have simple functions of counting paper money.
  • Paper money counters having functions of discriminating the genuineness or spuriousness and denominations of paper money and recognizing serial numbers of the paper money have been suggested.
  • Such conventional paper money counters having functions of recognizing serial numbers use a method of scanning and outputting a portion indicating a serial number or sensing only fragmentary characteristics of an image of the serial number to recognize letters.
  • the conventional paper money counters also use a method of extracting an image of a serial number of paper money and comparing the image with a reference letter image to recognize a letter sequence.
  • the scanned image is output as it is.
  • it is difficult to process the recognized serial number.
  • a recognition rate is deteriorated.
  • FIG. 1 is a flowchart illustrating a method of recognizing a serial number of paper money according to an embodiment of the present invention
  • FIG. 2 is a view illustrating a process of extracting a character sequence of a portion in which a serial number is inscribed in the method illustrated in FIG. 1 ;
  • FIG. 3 is a view illustrating an image of the character sequence extracted in the process of FIG. 2 and character portions into which the character sequence is divided;
  • FIG. 4 is a view illustrating a relationship between an output of a neural network and an error determination in the method illustrated in FIG. 1 ;
  • FIG. 5 is a schematic block diagram illustrating a configuration of an apparatus for recognizing a serial number of paper money according to an embodiment of the present invention
  • FIG. 6 is a view illustrating a configuration of a serial number recognition paper money counter according to an embodiment of the present invention.
  • the present invention also provides a method and an apparatus for detecting an error during a recognition of a serial number of paper money to determine a validity of a recognition of characters so as to recognize the serial number of the paper money.
  • an image of paper money can be input and processed to recognize a serial number of the paper money.
  • the serial number can be recognized as an output value so as to be further quickly recognized.
  • output values of a function can be compared during the recognition of the serial number to determine a validity of the recognition of the serial number and check an error. As a result, reliability of the recognition of the serial number can be improved.
  • FIG. 2 is a view illustrating operation S20 of the method illustrated in FIG. 1.
  • operation S21 the image of the paper money is obtained.
  • the obtained image includes portions besides the image of the paper money.
  • an outer line of the paper money is extracted to remove an image of such an unnecessary portion. Only an area enclosed by the outer line may be used.
  • only the character sequence of the portion showing the serial number of the paper money may be extracted in order to further reduce unnecessary processing.
  • a process of extracting only the character sequence will be described.
  • a center of the paper money to be used as a reference point is calculated to check a position of the character sequence of the portion showing the serial number.
  • operation S24 a direction and a distance are calculated based on the center to calculate position coordinates, and the image of the character sequence in the corresponding position is extracted.
  • FIG. 3 is a view illustrating the image of the character sequence extracted in the process of FIG. 2 and images of characters. Averages of pixels in a horizontal direction in the image of the character sequence are obtained. Coordinates in a point in which average values of the averages of the pixels are decreased and then increased are calculated. The character sequence is divided into the characters in a vertical direction based on the coordinates. A division is performed in the horizontal direction using the same method as that performed in the vertical direction to generate rectangular images of the divided characters.
  • input data is generated to substitute the images of the characters for a function that will be described later.
  • All of pixel values of the images of the characters may be generated as the input data or the images of the characters may be processed to generate the input data. If the all of the pixel values of the images of the characters are generated as the input data, the images of the characters are allocated to a frame having a predetermined standard pixel size so as to input the images of the characters into a function that will be described later.
  • Pixels of the frame are quantitated to be defined as values of the pixels and arranged in a series of numerical sequences. For example, gray scale values "0" through "255" are defined as the values of the quantitated pixels.
  • operation S50 the input data obtained in operation S40 is substituted for a neural network function.
  • a neural network is a known art, and thus its detailed description will be omitted.
  • Operation S40 is performed with respect to images obtained through combinations of numerical characters, letters, symbols, and reverse images of the numerical characters, the letters, and the symbols to be references for recognizing letters in order to generate basic character data.
  • the basic character data is learnt to provide the neural network in advance.
  • Boltzman machine learning method or a simulated annealing learning method may be used to learn the neural network. If the input data is substituted for the neural network function which has been learnt, probability values of the basic character data are output.
  • FIG. 4 is a view illustrating a relationship between an output of a neural network and an error determination in the method illustrated in FIG. 1.
  • input data is input into a neural network, and the neural network outputs probability values of basic characters.
  • the probability values are arranged in a descending order so as to use first and second probability values for validity. If the first probability value is not more than or equal to a predetermined probability value, it is determined that an error is present in a recognition of characters. If the first probability value is more than or equal to the predetermined probability value, the first probability value is compared with the second probability value. If an error between the first and second probability values is within a predetermined range, the recognition of the characters using the first and second probability values is not reliable.
  • FIG. 5 is a schematic block diagram illustrating a configuration of an apparatus for recognizing a serial number of paper money according to an embodiment of the present invention. Referring to FIG.
  • a serial number recognizing apparatus 100 includes a preprocessor 110, a neural network function processor 120, an error detector 130, and a storage unit 140.
  • the preprocessor 110 receives an image of paper money from an input unit 200 and processes the image.
  • the neural network function processor 120 applies the processed image to a neural network.
  • the error detector 130 detects an error present in a recognition of characters.
  • the storage unit 140 stores the processed image and basic character data.
  • the neural network function processor 120 substitutes the input data for a neural network function which has learnt basic data of each character, outputs probability values of the basic character data, and arranges the probability values in a descending order.
  • the neural network function processor 120 arranges characters corresponding to the basic character data corresponding to the first probability value and outputs a character sequence to the output unit 300.
  • the error detector 130 receives the probability values from the neural network function processor 120, calculates errors of the probability values to determine a validity, and outputs an error detection signal to the output unit 300.
  • the preprocessor 110 receives the image of the paper money from the input unit 200, converts the image into a digital image, and extracts the image of the portion on which the serial number is inscribed.
  • the preprocessor 110 divides the extracted image into images of characters, generates the input data using pixel values of the images of the characters, and stores the input data in the storage unit 140.
  • the neural network function processor 120 reads the input data from the storage unit 140, inputs the input data into the neural network function, arranges function values output from the neural network in a descending order, outputs the function values to the error detector 130 to determine whether the recognition of the characters is valid, and if the recognition of the characters is valid, transmits a character sequence corresponding to the corresponding function values to the output unit 300. Also, if the error d € ⁇ tector 130 determines that the error is present in the recognition of the characters, the error detector 130 outputs a serial number recognition error signal to the output unit 300.
  • FIG. 6 is a schematic view illustrating a configuration of a serial number recognition paper money counter according to an embodiment of the present invention.
  • the serial number recognition paper money counter includes an inlet
  • Paper money is put into the inlet 10.
  • the counter 20 counts the paper money.
  • the outlet 30 discharges the counted paper money.
  • the display 40 displays information as to the counted paper money.
  • the scanner 50 scans an image of the paper money.
  • the serial number recognizer 60 recognizes a serial number of the paper money.
  • the inlet 10 has a shape so as to accommodate a plurality of pieces of paper money.
  • the counter 20 counts a number of the plurality of pieces of paper money.
  • a roller that is rotating separates each piece from the plurality of pieces of paper money to count the number of the plurality of pieces of paper money.
  • the outlet 30 has a shape of a stand case in which each of the counted plurality of pieces of paper money is discharged and accumulated.
  • the display 40 is a display window displaying serial numbers of the plurality of pieces of paper money and information as to the counted number.
  • the scanner 50 includes an image sensor scanning images of the plurality of pieces of paper money.
  • the serial number recognizer 60 recognizes the serial numbers using the images extracted by the scanner 50.
  • the serial number recognizer 60 includes a function processor, a preprocessor, and a storage unit.
  • the function processor recognizes the serial numbers using a neural network function.
  • the preprocessor processes the extracted images to be input into the function processor to generate input data.
  • the storage unit stores the input data and data of the function processor.
  • the scanner 50 extracts images of the paper money.
  • the extracted images are transmitted to the serial number recognizer 60, and the paper money which has passed the scanner 50 moves to the counter 20.
  • the serial number recognizer 60 recognizes a character sequence of a serial number of the paper money and d transmits information as to the serial number of the paper money to the display 40.
  • the paper money moved to the counter 20 is continuously counted and discharged to the outlet 30 to be accumulated as a bundle of paper money.
  • information as to the counted paper money is transmitted to the display 40.
  • the display 40 converts the information transmitted from the serial number recognizer 60 and the counter 20 into information which can be checked by a user and displays the converted information.
  • a method of recognizing a serial number of paper money including: receiving an image of paper money including a plurality of figures and a plurality of characters and extracting an image of a character sequence of a portion on which the serial number is inscribed; dividing the image of the character sequence into character images having a predetermined pixel size; allocating the character images to an area having a predetermined pixel size, converting values of quantitated pixels of the area into numerical values, and arranging the numerical values in a series of numerical sequences in order to generate input data; inputting the input data into a neural network which has learnt a plurality of pieces of basic character data of each character, so as to allow the input data to coincide with the plurality of pieces of basic character data of each character and outputting probability values depending on the number of cases of the plurality of pieces of basic character data; and arranging the probability values output from the neural network in descending order, selecting a case according to first basic character data having the highest probability value,
  • the method may further include determining whether an error is present using first and second probability values of the probability values in order to determine the validity of a recognition of characters and outputting error detection data.
  • the plurality of pieces of basic character data may be obtained through combinations of numerals, characters, symbols, and reverse characters.
  • a character image of the area may be divided into blocks having predetermined sizes, pixel values of the blocks may be averaged, and input data may be generated using average values of the blocks.
  • the receiving of the image of the paper money comprising the plurality of figures and the plurality of characters and the extracting of the image of the character sequence of the portion on which the serial number is inscribed may include: obtaining the image of the paper money; extracting an outer line of the obtained image; and extracting an area in which the serial number is positioned as the character image based on a center of the image.
  • an apparatus for recognizing a serial number of paper money including: a preprocessor receiving an image of paper money including a plurality of figures and a plurality of characters, extracting an image of a character sequence of a portion on which the serial number is inscribed, dividing the image into character images, and generating pixel values of the character images as input data; a function processor inputting the input data into a neural network which has learnt a plurality of pieces of basic character data of each character so as to allow the input data to coincide with the plurality of pieces of basic character data, outputting probability values depending on a number of cases of each character, arranging the probability values in descending order, selecting a case depending on first basic character data having the highest probability value, sequentially arranging characters corresponding to the case, and outputting the characters as a character sequence; and a storage unit storing the input data and basic character data preprocessed by the preprocessor.
  • a computer-readable recording medium having embodied thereon a computer program for executing the method.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Security & Cryptography (AREA)
  • Inspection Of Paper Currency And Valuable Securities (AREA)
  • Character Discrimination (AREA)

Abstract

L'invention porte sur un procédé et sur un appareil permettant de reconnaître un numéro de série de papier-monnaie. Le procédé consiste à: recevoir une image du papier-monnaie comportant une pluralité de chiffres et de caractères, extraire une image d'une séquence de caractères sur une partie de laquelle est inscrit le numéro de série; diviser l'image de la séquence de caractères en images de caractères ayant une taille prédéterminée de pixel; affecter les images des caractères à une zone ayant une taille de pixel prédéterminée, convertir des valeurs de pixel quantifiées de la zone en valeurs numériques et agencer les valeurs en une série de séquences numériques pour générer des données d'entrée; introduire les données d'entrée dans un réseau neuronal qui a appris une pluralité de pièces de données de caractères basiques de chaque caractère pour pouvoir faire coïncider les données d'entrée avec la pluralité de pièces de données de caractères basiques de chaque caractère et émettre des valeurs de probabilité en fonction d'un nombre de cases de la pluralité des pièces de données de caractères basiques; et agencer les valeurs de probabilité émises par le réseau neuronal dans un ordre décroissant, sélectionner une case en fonction des premières données de caractères basiques ayant la plus haute valeur de probabilité, agencer de manière séquentielle des caractères correspondant à la case et émettre des caractères sous forme d'une séquence de caractères.
PCT/KR2006/002879 2005-07-21 2006-07-21 Procede et appareil pour reconnaitre un numero de serie de papier-monnaie WO2007011188A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
KR1020050066499A KR100719608B1 (ko) 2005-07-21 2005-07-21 지폐 일련번호 인식 방법 및 장치
KR10-2005-0066499 2005-07-21

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KR (1) KR100719608B1 (fr)
TR (1) TR200800352T1 (fr)
WO (1) WO2007011188A1 (fr)

Cited By (7)

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US9563811B2 (en) 2012-07-31 2017-02-07 Lg Cns Co., Ltd. Character recognition method, character recognition apparatus and financial apparatus
JP2019171136A (ja) * 2015-08-03 2019-10-10 エンゼルプレイングカード株式会社 遊技場における不正検知システム
US20200005067A1 (en) * 2018-06-27 2020-01-02 Hyundai Motor Company Identification code recognition system and method
WO2021162208A1 (fr) * 2020-02-12 2021-08-19 주식회사 페이콕 Appareil de paiement et son procédé de commande
US11410494B2 (en) 2016-08-02 2022-08-09 Angel Group Co., Ltd. Game management system
US20220301328A1 (en) * 2019-12-13 2022-09-22 Huawei Technologies Co., Ltd. Text recognition method and apparatus
CN112840383B (zh) * 2018-10-24 2024-03-08 富士通先端科技株式会社 纸币检查装置、纸币检查方法及纸币检查程序

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KR101337022B1 (ko) * 2012-07-31 2013-12-05 주식회사 엘지씨엔에스 금융기기, 매체감지장치 및 매체감지방법
KR102094234B1 (ko) * 2018-04-12 2020-03-30 주식회사 푸른기술 지폐 일련번호 인식 장치 및 방법
KR102123910B1 (ko) * 2018-04-12 2020-06-18 주식회사 푸른기술 머신 러닝을 이용한 지폐 일련번호 인식 장치 및 방법
CN110490031B (zh) * 2018-05-15 2022-08-16 武汉斗鱼网络科技有限公司 一种通用数字识别的方法、存储介质、电子设备及系统
KR102368065B1 (ko) * 2020-04-09 2022-03-02 엘케이시스(주) 컨테이너 문자 인식 방법 및 장치
KR102632091B1 (ko) * 2021-02-05 2024-02-05 상명대학교산학협력단 영상 인식 기반 지폐 일련번호 인식 장치 및 방법

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US9563811B2 (en) 2012-07-31 2017-02-07 Lg Cns Co., Ltd. Character recognition method, character recognition apparatus and financial apparatus
US11393286B2 (en) 2015-08-03 2022-07-19 Angel Group Co., Ltd. Fraud detection system in a casino
US11741780B2 (en) 2015-08-03 2023-08-29 Angel Group Co., Ltd. Fraud detection system in a casino
US10846985B2 (en) 2015-08-03 2020-11-24 Angel Playing Cards Co., Ltd. Fraud detection system in a casino
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US10896575B2 (en) 2015-08-03 2021-01-19 Angel Playing Cards Co., Ltd. Fraud detection system in a casino
US10930112B2 (en) 2015-08-03 2021-02-23 Angel Playing Cards Co., Ltd. Fraud detection system in casino
US11037401B2 (en) 2015-08-03 2021-06-15 Angel Playing Cards Co., Ltd. Fraud detection system in a casino
US11393285B2 (en) 2015-08-03 2022-07-19 Angel Group Co., Ltd. Fraud detection system in a casino
US11380161B2 (en) 2015-08-03 2022-07-05 Angel Group Co., Ltd. Fraud detection system in a casino
US11386749B2 (en) 2015-08-03 2022-07-12 Angel Group Co., Ltd. Fraud detection system in a casino
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JP2019171136A (ja) * 2015-08-03 2019-10-10 エンゼルプレイングカード株式会社 遊技場における不正検知システム
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US11527131B2 (en) 2015-08-03 2022-12-13 Angel Group Co., Ltd. Fraud detection system in a casino
US11657673B2 (en) 2015-08-03 2023-05-23 Angel Group Co., Ltd. Fraud detection system in a casino
US11657674B2 (en) 2015-08-03 2023-05-23 Angel Group Go., Ltd. Fraud detection system in casino
JP7178964B2 (ja) 2015-08-03 2022-11-28 エンゼルグループ株式会社 遊技場における不正検知システム
US11393284B2 (en) 2015-08-03 2022-07-19 Angel Group Co., Ltd. Fraud detection system in a casino
US11527130B2 (en) 2015-08-03 2022-12-13 Angel Group Co., Ltd. Fraud detection system in a casino
US11587398B2 (en) 2015-08-03 2023-02-21 Angel Group Co., Ltd. Fraud detection system in a casino
US11620872B2 (en) 2015-08-03 2023-04-04 Angel Group Co., Ltd. Fraud detection system in a casino
US11410494B2 (en) 2016-08-02 2022-08-09 Angel Group Co., Ltd. Game management system
US20200005067A1 (en) * 2018-06-27 2020-01-02 Hyundai Motor Company Identification code recognition system and method
CN112840383B (zh) * 2018-10-24 2024-03-08 富士通先端科技株式会社 纸币检查装置、纸币检查方法及纸币检查程序
US20220301328A1 (en) * 2019-12-13 2022-09-22 Huawei Technologies Co., Ltd. Text recognition method and apparatus
US11893767B2 (en) * 2019-12-13 2024-02-06 Huawei Technologies Co., Ltd. Text recognition method and apparatus
WO2021162208A1 (fr) * 2020-02-12 2021-08-19 주식회사 페이콕 Appareil de paiement et son procédé de commande

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Publication number Publication date
KR20070011867A (ko) 2007-01-25
KR100719608B1 (ko) 2007-05-17
TR200800352T1 (tr) 2008-12-22

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