Skip to main content
Erschienen in: Neural Computing and Applications 3/2021

03.08.2020 | S.I. : ATCI 2020

Image pattern recognition in identification of financial bills risk management

verfasst von: Huahua Li, Chuanchao Huang, Lihan Gu

Erschienen in: Neural Computing and Applications | Ausgabe 3/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The automatic identification system of financial bills needs to have high recognition rate, high anti-interference and real-time to ensure its recognition effect. Based on the image recognition theory, this study uses the differential projection method to define the boundary of the bill. The horizontal projection gradation of the boundary area of the bill character line is significantly reduced from large to small, and the horizontal difference projection of the grayscale image can be performed to locate the boundary of the bill image. The word height of bills studied in this paper is constant, the character line spacing is equal, and the horizontal differential projection is used to realize row positioning. The main process of the system is to first select the check image, and then the software part realizes the process of image analysis, preprocessing, character segmentation, feature extraction, character recognition, etc. Finally, the recognized amount is output to the interface. It is proved by experiments that the recognition rate of this algorithm is high, which can provide theoretical reference for subsequent related research.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Ma X, Yang R, Zou D et al (2020) Measuring extreme risk of sustainable financial system using GJR-GARCH model trading data-based. Int J Inf Manag 50:526–537CrossRef Ma X, Yang R, Zou D et al (2020) Measuring extreme risk of sustainable financial system using GJR-GARCH model trading data-based. Int J Inf Manag 50:526–537CrossRef
2.
Zurück zum Zitat Zhang XY, Bengio Y, Liu CL (2017) Online and offline handwritten Chinese character recognition: a comprehensive study and new benchmark. Pattern Recognit 61(Complete):348–360CrossRef Zhang XY, Bengio Y, Liu CL (2017) Online and offline handwritten Chinese character recognition: a comprehensive study and new benchmark. Pattern Recognit 61(Complete):348–360CrossRef
3.
Zurück zum Zitat Zhong Z, Jin L, Xie Z (2015) High performance offline handwritten Chinese character recognition using Googlenet and directional feature maps. In: 2015 13th International conference on document analysis and recognition (ICDAR). IEEE, pp 846–850 Zhong Z, Jin L, Xie Z (2015) High performance offline handwritten Chinese character recognition using Googlenet and directional feature maps. In: 2015 13th International conference on document analysis and recognition (ICDAR). IEEE, pp 846–850
4.
Zurück zum Zitat Chatterji BN (1986) Feature extraction methods for character recognition. IETE Tech Rev 3(1):9–22CrossRef Chatterji BN (1986) Feature extraction methods for character recognition. IETE Tech Rev 3(1):9–22CrossRef
5.
Zurück zum Zitat Pengchao LI, Liangrui P, Juan W (2016) Rejecting character recognition errors using CNN based confidence estimation. Chin J Electron 25(3):520–526CrossRef Pengchao LI, Liangrui P, Juan W (2016) Rejecting character recognition errors using CNN based confidence estimation. Chin J Electron 25(3):520–526CrossRef
6.
Zurück zum Zitat Liu W, Chen C, Wong KYK (2018) Char-net: a character-aware neural network for distorted scene text recognition. In: Thirty-second AAAI conference on artificial intelligence Liu W, Chen C, Wong KYK (2018) Char-net: a character-aware neural network for distorted scene text recognition. In: Thirty-second AAAI conference on artificial intelligence
7.
Zurück zum Zitat Sarkhel R, Das N, Saha AK et al (2016) A multi-objective approach towards cost effective isolated handwritten Bangla character and digit recognition. Pattern Recogn 58:172–189CrossRef Sarkhel R, Das N, Saha AK et al (2016) A multi-objective approach towards cost effective isolated handwritten Bangla character and digit recognition. Pattern Recogn 58:172–189CrossRef
8.
Zurück zum Zitat He M, Zhang S, Mao H et al (2015) Recognition confidence analysis of handwritten Chinese character with CNN. In: 2015 13th international conference on document analysis and recognition (ICDAR). IEEE, pp 61–65 He M, Zhang S, Mao H et al (2015) Recognition confidence analysis of handwritten Chinese character with CNN. In: 2015 13th international conference on document analysis and recognition (ICDAR). IEEE, pp 61–65
9.
Zurück zum Zitat Prasad JR, Kulkarni U (2015) Gujrati character recognition using weighted k-NN and mean χ2 distance measure. Int J Mach Learn Cybern 6(1):69–82CrossRef Prasad JR, Kulkarni U (2015) Gujrati character recognition using weighted k-NN and mean χ2 distance measure. Int J Mach Learn Cybern 6(1):69–82CrossRef
10.
Zurück zum Zitat Chaudhuri A, Mandaviya K, Badelia P et al (2017) Optical character recognition systems for optical character recognition systems for different languages with soft computing. Springer, Cham, pp 9–41CrossRef Chaudhuri A, Mandaviya K, Badelia P et al (2017) Optical character recognition systems for optical character recognition systems for different languages with soft computing. Springer, Cham, pp 9–41CrossRef
11.
Zurück zum Zitat Jin LW, Zhong ZY, Yang Z et al (2016) Applications of deep learning for handwritten chinese character recognition: a review. Acta Autom Sin 42(8):1125–1141MATH Jin LW, Zhong ZY, Yang Z et al (2016) Applications of deep learning for handwritten chinese character recognition: a review. Acta Autom Sin 42(8):1125–1141MATH
12.
Zurück zum Zitat Gunawan D, Arisandi D, Ginting FM et al (2017) Russian character recognition using self-organizing map. J Phys Conf Ser 801:012040CrossRef Gunawan D, Arisandi D, Ginting FM et al (2017) Russian character recognition using self-organizing map. J Phys Conf Ser 801:012040CrossRef
13.
Zurück zum Zitat Alvarez D, Fernandez R, Sanchez L (2015) Stroke-based intelligent character recognition using a deterministic finite automaton. Log J IGPL 23(3):463–471MathSciNetCrossRef Alvarez D, Fernandez R, Sanchez L (2015) Stroke-based intelligent character recognition using a deterministic finite automaton. Log J IGPL 23(3):463–471MathSciNetCrossRef
14.
Zurück zum Zitat Ahmad I, Wang X, Li R et al (2017) Offline Urdu Nastaleeq optical character recognition based on stacked denoising autoencoder. China Commun 14(1):146–157CrossRef Ahmad I, Wang X, Li R et al (2017) Offline Urdu Nastaleeq optical character recognition based on stacked denoising autoencoder. China Commun 14(1):146–157CrossRef
15.
Zurück zum Zitat He X, Chen L, Li X et al (2019) Brain image feature recognition method for Alzheimer’s disease. Cluster Comput 22(4):8109–8117CrossRef He X, Chen L, Li X et al (2019) Brain image feature recognition method for Alzheimer’s disease. Cluster Comput 22(4):8109–8117CrossRef
16.
Zurück zum Zitat Cui Y, Ma Y (2017) Progress and survey of mobile image feature recognition. Recent Pat Comput Sci 10(1):6–15MathSciNet Cui Y, Ma Y (2017) Progress and survey of mobile image feature recognition. Recent Pat Comput Sci 10(1):6–15MathSciNet
17.
Zurück zum Zitat Sharan RV, Moir TJ (2018) Pseudo-color cochleagram image feature and sequential feature selection for robust acoustic event recognition. Appl Acoust 140:198–204CrossRef Sharan RV, Moir TJ (2018) Pseudo-color cochleagram image feature and sequential feature selection for robust acoustic event recognition. Appl Acoust 140:198–204CrossRef
18.
Zurück zum Zitat Wang Z, Liu P, Cui T (2017) Research on forest flame recognition algorithm based on image feature. ISPRS Int Arch Photogramm Remote Sens Spatial Inf Sci XLII(2/W7):925–928CrossRef Wang Z, Liu P, Cui T (2017) Research on forest flame recognition algorithm based on image feature. ISPRS Int Arch Photogramm Remote Sens Spatial Inf Sci XLII(2/W7):925–928CrossRef
19.
Zurück zum Zitat Yan B, Tan W, Li K et al (2017) Codebook guided feature-preserving for recognition-oriented image retargeting. IEEE Trans Image Process 26(5):2454–2465MathSciNetCrossRef Yan B, Tan W, Li K et al (2017) Codebook guided feature-preserving for recognition-oriented image retargeting. IEEE Trans Image Process 26(5):2454–2465MathSciNetCrossRef
20.
Zurück zum Zitat Yan Y, Huang C, Wang Q et al (2020) Data mining of customer choice behavior in internet of things within relationship network. Int J Inf Manag 50:566–574CrossRef Yan Y, Huang C, Wang Q et al (2020) Data mining of customer choice behavior in internet of things within relationship network. Int J Inf Manag 50:566–574CrossRef
Metadaten
Titel
Image pattern recognition in identification of financial bills risk management
verfasst von
Huahua Li
Chuanchao Huang
Lihan Gu
Publikationsdatum
03.08.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 3/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05261-3

Weitere Artikel der Ausgabe 3/2021

Neural Computing and Applications 3/2021 Zur Ausgabe